Generating digital assets utilizing a content aware machine-learning model

ABSTRACT

The present disclosure describes methods, systems, and non-transitory computer-readable media for implementing a machine learning framework to generate a recommend digital assets from a digital image. For example, in one or more embodiments, the disclosed systems utilize a machine learning model to detect a shape, color, pattern, or other digital asset type from a digital image and then extract (and further modify) the detected asset type to create various different digital assets as recommendations. In some cases, the disclosed system utilizes the machine learning model to determine one or more digital asset classes associated with the digital image, generate preprocessed digital assets from the digital image for those digital asset classes, and generate production-ready digital assets from the preprocessed digital assets. Further, in some instances, the disclosed systems provide one or more of the digital assets via recommendations based on asset scores determined via the generation process.

BACKGROUND

In recent years, computer-implemented technologies have improvedsoftware platforms for generating digital visual content. For instance,many conventional digital asset generation systems can create originaldigital visual content by incorporating one or more visual elementsknown as digital assets (e.g., objects, colors, fonts). Someconventional digital asset generation systems provide tools whereby userdevices can create the digital assets themselves. Indeed, suchconventional systems can provide software tools that facilitate thecreation of digital assets from the ground up or using some template. Toillustrate, some conventional systems provide tools for generating oneor more digital assets from a digital image that depicts or is otherwiseassociated with the digital asset(s). For instance, some conventionaldigital asset generation systems can extract an object from a digitalimage and provide the object within a template from which a user devicecan edit and add other imagery to create a pattern or other digitalasset. Although conventional systems can provide tools for digital assetgeneration, as explained further below, they typically rely on difficultand tedious interactive procedures and require use of multiple separategraphical user interfaces and computational models to generate differentdigital assets, resulting in inefficient operation.

SUMMARY

This disclosure describes one or more embodiments of methods,non-transitory computer-readable media, and systems that solve one ormore of the foregoing problems and provide other benefits. For example,in one or more embodiments, the disclosed systems utilize a machinelearning model to detect a shape, color, pattern, or other digital assettype from a digital image and then extract (and further modify) thedetected asset type to create various different digital assets asrecommendations. To illustrate, in some implementations, the disclosedsystems implement a machine learning model to determine an asset typethat is associated with a digital image from various asset types. Thedisclosed systems also utilize the machine learning model to generate,from the digital image, a digital asset of the asset type and providethe digital asset to a client device as part of an asset recommendation.The asset recommendation may include or incorporate various differenttypes, including a shape, color palette, color gradient, pattern, font,or others noted below.

In some cases, the disclosed systems utilize the machine learning modelto generate multiple digital assets of different asset types from thedigital image, score the digital assets via the generation process, rankthe digital assets based on their scores, and utilize the ranking toselect one or more of the digital assets for recommendation to a clientdevice. Thus, the disclosed systems introduce an unconventional approachthat utilizes machine learning to efficiently generate digital assetsfrom digital images. Additional features and advantages of one or moreembodiments of the present disclosure are outlined in the followingdescription.

BRIEF DESCRIPTION OF THE DRAWINGS

This disclosure will describe one or more embodiments of the inventionwith additional specificity and detail by referencing the accompanyingfigures. The following paragraphs briefly describe those figures, inwhich:

FIG. 1 illustrates an example system environment in which a digitalasset recommendation system can operate in accordance with one or moreembodiments.

FIG. 2 illustrates an overview diagram of the digital assetrecommendation system generating recommended digital assets from adigital image in accordance with one or more embodiments.

FIG. 3 illustrates an architecture of anasset-recommendation-machine-learning model in accordance with one ormore embodiments.

FIGS. 4A-4E illustrate diagrams for utilizing components of anasset-recommendation-machine-learning model to generate various digitalassets in accordance with one or more embodiments.

FIG. 5 illustrates a diagram for training anasset-recommendation-machine-learning model in accordance with one ormore embodiments.

FIG. 6 illustrates example digital assets generated from various digitalimages in accordance with one or more embodiments.

FIG. 7 illustrates an example schematic diagram of a digital assetrecommendation system in accordance with one or more embodiments.

FIG. 8 illustrates a flowchart of a series of acts for generating adigital asset for recommendation from a digital image in accordance withone or more embodiments.

FIG. 9 illustrates a block diagram of an exemplary computing device inaccordance with one or more embodiments.

DETAILED DESCRIPTION

The disclosure describes one or more embodiments of a digital assetrecommendation system that utilizes a machine learning model to detect,generate, and recommend different types of digital assets from a digitalimage. In one or more embodiments, the machine learning model implementsone or more subunits, such as a classifier for determining asset typesassociated with a digital image, one or more specialized assets networksfor identifying interest areas of the digital image and generatingpre-assets (e.g., pre-configured digital assets), and one or moreadditional classifiers for determining configurations to generatedigital assets. In some cases, the machine learning model furtherincludes an intelligent ranking unit (IRU) that ranks the digital assetsbased on scores determined throughout the generation process and selectsone or more of the digital assets for recommending to a client devicebased on the ranking. Thus, in one or more embodiments, the digitalasset recommendation system utilizes the machine learning model forend-to-end creation and recommendation of production-ready digitalassets based on analysis of a digital image.

To provide an illustration, in one or more embodiments, the digitalasset recommendation system determines, utilizing anasset-recommendation-machine-learning model, a digital asset classassociated with a digital image from among a set of different digitalasset classes. Additionally, the digital asset recommendation systemgenerates, from the digital image and utilizing theasset-recommendation-machine-learning model, a digital assetcorresponding to the digital asset class. The digital assetrecommendation system further generates, from the digital asset, arecommended digital asset associated with the digital asset class.

As just mentioned, in one or more embodiments, the digital assetrecommendation system identifies, generates, and recommends one or moredigital assets from a digital image. In some cases, the generateddigital assets are associated with one or more digital asset classes(e.g., digital asset types). To illustrate, in some embodiments, thedigital asset recommendation system generates, from the digital image, ashape asset corresponding to a shape asset class, a color palette assetcorresponding to a color asset class, a color gradient assetcorresponding to the color asset class, a pattern asset corresponding toa pattern asset class, a font asset corresponding to a font asset class,or a font theme asset corresponding to the font asset class.

As further mentioned above, in some embodiments, theasset-recommendation-machine-learning model includes various differentnetworks or models for identifying, generating, and selecting digitalassets for recommendation to a client device. For instance, in one orone embodiments, the digital asset recommendation system utilizes anasset-classification-neural network of theasset-recommendation-machine-learning model to determine one or moredigital asset classes associated with the digital image. In someembodiments, the asset-classification-neural network generates aclassification metric for each of a plurality of digital assetclasses—such as the shape asset class, the pattern asset class, and thecolor asset class—to indicate a likelihood that the digital image isassociated with the digital asset class (e.g., the digital image isusable for generating a digital asset from that digital asset class). Insome cases, the digital asset recommendation system utilizes a separatefont classification model of the asset-recommendation-machine-learningmodel to generate a classification metric for the font asset class. Insome implementations, the digital asset recommendation system determinesthat a digital asset class is associated with the digital image based onthe classification metric for the digital asset class satisfying athreshold value.

In addition to an asset-classification-neural network, in one or moreembodiments, the digital asset recommendation system utilizes one ormore pre-asset networks of the asset-recommendation-machine-learningmodel to generate one or more preprocessed digital assets for thedigital asset classes associated with the digital image. For instance,in some embodiments, the digital asset recommendation system utilizesthe one or more pre-asset networks to generate a preprocessed shapeasset corresponding to the shape asset class or the pattern asset classby identifying and extracting an object portrayed in the digital image.In some cases, the digital asset recommendation system utilizes the oneor more pre-asset networks to generate a preprocessed color assetcorresponding to the color class by generating a foreground image layerand/or a background image layer from the digital image. In someinstances, the digital asset recommendation system further utilizes theone or more pre-asset networks to generate a font asset corresponding tothe font class based the height and/or length of text depicted in thedigital image.

In addition to an asset-classification-neural network and one or morepre-asset networks, in one or more embodiments, the digital assetrecommendation system further utilizes one or moreasset-configuration-neural networks of theasset-recommendation-machine-learning model to generate digital assetsfrom the preprocessed digital assets. For instance, in some cases, thedigital asset recommendation system utilizes anasset-configuration-neural network to generate a shape asset or apattern asset from a preprocessed shape asset. In some implementations,the digital asset recommendation system utilizes anasset-configuration-neural network to generate a color palette asset ora color gradient asset from a preprocessed color asset.

In addition to utilizing various internal networks noted above, in someembodiments, the digital asset recommendation system utilizes theasset-recommendation-machine-learning model to determine one or morerecommended digital assets from the generated digital assets. Toillustrate, in some cases, the asset-recommendation-machine-learningmodel determines an asset score for each of the generated digital assetsand ranks the digital assets based on their asset scores. Further, theasset-recommendation-machine-learning model selects one or more digitalassets to provide as recommendations to a client device using theranking.

In one or more embodiments, the digital asset recommendation systemprovides the recommendations that include the selected digital assetsfor display within a graphical user interface of the client device thatalso displays a digital asset created by the client device from thedigital image. Indeed, in some cases, the digital asset recommendationsystem detects one or more user interactions with the graphical userinterface for creating a digital asset from a digital image.Accordingly, the digital asset recommendation system implements theasset-recommendation-machine-learning model to identify, generate, andrecommend one or more additional digital assets and provides therecommendation(s) for display within the graphical user interface.

In some cases, the digital asset recommendation system implements theasset-recommendation-machine-learning model to provide various otherfeatures. As one example, upon identifying a digital asset classassociated with a digital image via theasset-recommendation-machine-learning model, the digital assetrecommendation system provides, to a client device, one or moreinteractive elements for generating a digital asset of the digital assetclass from the digital image. Accordingly, the digital assetrecommendation system utilizes the asset-recommendation-machine-learningmodel to facilitate device-interactive creation of a digital asset viathe client device.

As mentioned above, conventional digital asset generation systems sufferfrom technological shortcomings that result in inefficient operation. Inparticular, conventional digital asset generation systems typically relyon labor-intensive interactive procedures for generating a digital assetfrom a digital image. Such procedures often require multiple steps ofusers interacting with a graphical user interface to identify, create,edit, and save digital assets for subsequent use. Some conventionalsystems require separate graphical user interfaces for each step in theprocess (e.g., a graphical user interface for identifying a digitalasset, one or more graphical user interfaces for creating the digitalasset). Thus, these conventional systems provide inefficient digitalasset generation processes having significant turnaround time andinteraction before a digital asset is ready to use.

Further, conventional digital asset generation systems often utilizedifferent computational models for generating digital assets ofdifferent types, exacerbating the efficiency problems. Indeed, manyconventional systems implement a dedicated set of computational modelsfor identifying and providing tools to create a digital asset of aparticular type. Accordingly, these systems typically require a clientdevice to open and execute separate sets of models or applications tocreate multiple digital assets from a digital image, where some of thedigital assets are of a different type or class. Such systems consume asignificant amount of computing resources in the opening and executionof these separate models.

The digital asset recommendation system provides several advantages overconventional systems. For example, the digital asset recommendationsystem provides for improved efficiency by reducing the userinteractions required for generating digital assets from digital images.In particular, by implementing an asset-recommendation-machine-learningmodel to identify, generate, and recommend digital assets, the digitalasset recommendation system provides a user interface for preparing andsaving production-ready digital assets with reduced user interactions.Indeed, with only a few user interactions, the digital assetrecommendation system can generate multiple digital assets from adigital image where conventional systems would typically require manyadditional user interactions to generate a single digital asset from thesame digital image. Further, by implementing theasset-recommendation-machine-learning model, the digital assetrecommendation system can generate one or more digital assets from adigital image without requiring navigation through multiple graphicaluser interfaces, computational models, or applications dedicated toperforming a particular task (e.g., identifying a digital asset) ordedicated to a particular asset type (e.g., only font assets).Accordingly, the digital asset recommendation system provides a moreefficient digital asset generation process with reduced turnaround time,reduced interaction and navigation, and reduced consumption of computingresources.

Additionally, the digital asset recommendation system provides improvedflexibility and functionality when compared to conventional digitalasset generation systems by generating recommended digital assets ofdifferent digital asset classes. While conventional systems typicallyimplement computational models that are limited to generating a digitalasset of a particular digital asset type (e.g., only font assets or onlycolor assets), the digital asset recommendation system flexiblygenerates digital assets from multiple digital asset classes. Forinstance, the digital asset recommendation system can (i) generatedigital assets of different digital asset classes from different digitalimages or (ii) generate multiple digital assets from different digitalasset classes using a single digital image. The digital assetrecommendation system provides such flexibility by implementing variousinternal networks and models of an asset-recommendation-machine-learningmodel to intelligently detect the contents of a digital image, determinepotential digital asset classes corresponding to the contents, andgenerate digital assets from those digital asset classes using selectedinternal networks and models.

Further, the digital asset recommendation system introduces anunconventional approach for creating production-ready digital assetsfrom a digital image. In particular, the digital asset recommendationsystem utilizes an unconventional ordered combination of actions foridentifying, creating, and recommending digital assets from a digitalimage via a machine learning model. Indeed, the digital assetrecommendation system utilizes an asset-recommendation-machine-learningmodel to determine which types of digital assets can be generated from adigital image, generates one or more digital assets that are of thosetypes of digital assets, and determine recommendable digital assets fromthe generated digital assets. Thus, the digital asset recommendationsystem utilizes machine learning to provide a client device withrecommendations that include pre-generated digital assets that areproduction ready. Further, by utilizing theasset-recommendation-machine-learning model to identify and generatedigital assets, the digital asset recommendation system provides optionsfor digital assets that may ordinarily by unrecognized by users.

Additional detail regarding the digital asset recommendation system willnow be provided with reference to the figures. For example, FIG. 1illustrates a schematic diagram of an exemplary system 100 in which adigital asset recommendation system 106 operates. As illustrated in FIG.1 , the system 100 includes a server(s) 102, a network 108, and clientdevices 110 a-110 n.

Although the system 100 of FIG. 1 is depicted as having a particularnumber of components, the system 100 is capable of having any number ofadditional or alternative components (e.g., any number of servers,client devices, or other components in communication with the digitalasset recommendation system 106 via the network 108). Similarly,although FIG. 1 illustrates a particular arrangement of the server(s)102, the network 108, and the client devices 110 a-110 n, variousadditional arrangements are possible.

The server(s) 102, the network 108, and the client devices 110 a-110 nare communicatively coupled with each other either directly orindirectly (e.g., through the network 108 discussed in greater detailbelow in relation to FIG. 9 ). Moreover, the server(s) 102 and theclient devices 110 a-110 n include one of a variety of computing devices(including one or more computing devices as discussed in greater detailwith relation to FIG. 9 ).

As mentioned above, the system 100 includes the server(s) 102. In one ormore embodiments, the server(s) 102 generates, stores, receives, and/ortransmits data, including digital images and digital assets created fromdigital images. For example, in some embodiments, the server(s) 102receives a digital image from a client device (e.g., one of the clientdevices 110 a-110 n) and transmits a digital asset created using thedigital image to the client device in return. In one or moreembodiments, the server(s) 102 comprises a data server. In someimplementations, the server(s) 102 comprises a communication server or aweb-hosting server.

As shown in FIG. 1 , the server(s) 102 includes a visual design system104. In one or more embodiments, the visual design system 104 providesfunctionality by which a client device (e.g., one of the client devices110 a-110 n) generates, edits, manages, and/or stores visual designs,such as digital graphic designs, modified digital photographs, digitallycreated art, etc. For example, in some implementations, a client devicecreates a canvas for generating a visual design via the visual designsystem 104. The visual design system 104 then provides many options forthe client device to use in creating a visual design, such as byapplying one or more digital assets to the canvas.

Additionally, the server(s) 102 include the digital asset recommendationsystem 106. In particular, in one or more embodiments, the digital assetrecommendation system 106 utilizes the server(s) 102 to generate one ormore digital assets from a digital image. For example, in some cases,the digital asset recommendation system 106 utilizes the servers toreceive a digital image, create one or more digital assets from thedigital image, and provide a recommendation including at least one ofthe digital assets. As shown in FIG. 1 , the digital assetrecommendation system 106 includes theasset-recommendation-machine-learning model 114. In some cases, thedigital asset recommendation system 106 utilizes the server(s) 102 togenerate and recommend the one or more digital assets via theasset-recommendation-machine-learning model 114.

To illustrate, in one or more embodiments, the digital assetrecommendation system 106, via the server(s) 102, determines a digitalasset class associated with a digital image from among a set ofdifferent digital asset classes utilizing theasset-recommendation-machine-learning model 114. Further, via theserver(s) 102, the digital asset recommendation system 106 generates adigital asset corresponding to the digital asset class from the digitalimage and utilizing the asset-recommendation-machine-learning model 114.Via the server(s) 102, the digital asset recommendation system 106further generates a recommended digital asset associated with thedigital asset class from the digital asset.

In one or more embodiments, the client devices 110 a-110 n includecomputing devices that are capable of generating digital assets fromdigital images. For example, the client devices 110 a-110 n include oneor more of smartphones, tablets, desktop computers, laptop computers,head-mounted-display devices, and/or other electronic devices. In someinstances, the client devices 110 a-110 n include one or moreapplications (e.g., the visual design applications 112 a-112 n,respectively) that are capable of generating digital assets from digitalimages. For example, in one or more embodiments, the visual designapplications 112 a-112 n include a software application installed on theclient devices 110 a-110 n, respectively. Additionally, oralternatively, the visual design applications 112 a-112 n include asoftware application hosted on the server(s) 102 (and supported by thevisual design system 104), which is accessible by the client devices 110a-110 n, respectively, through another application, such as a webbrowser.

In particular, in some implementations, the digital asset recommendationsystem 106 on the server(s) 102 supports the digital assetrecommendation system 106 on the client device 110 n. For instance, thedigital asset recommendation system 106 on the server(s) 102 learnsparameters for the asset-recommendation-machine-learning model 114. Thedigital asset recommendation system 106 then, via the server(s) 102,provides the asset-recommendation-machine-learning model 114 to theclient device 110 n. In other words, the client device 110 n obtains(e.g., downloads) the asset-recommendation-machine-learning model 114with the learned parameters from the server(s) 102. Once downloaded, thedigital asset recommendation system 106 on the client device 110 n isable to utilize the asset-recommendation-machine-learning model 114 togenerate digital assets from digital images independent from theserver(s) 102.

In alternative implementations, the digital asset recommendation system106 includes a web hosting application that allows the client device 110n to interact with content and services hosted on the server(s) 102. Toillustrate, in one or more implementations, the client device 110 naccesses a web page supported by the server(s) 102. The client device110 n provides a digital image to the server(s) 102, and, in response,the digital asset recommendation system 106 on the server(s) 102generates one or more digital assets from the digital image. Theserver(s) 102 then provides the digital asset(s) to the client device110 n for implementation or further editing.

Indeed, the digital asset recommendation system 106 is able to beimplemented in whole, or in part, by the individual elements of thesystem 100. Indeed, although FIG. 1 illustrates the digital assetrecommendation system 106 implemented with regard to the server(s) 102,different components of the digital asset recommendation system 106 canbe implemented by a variety of devices within the system 100. Forexample, in one or more implementations, one or more (or all) componentsof the digital asset recommendation system 106 are implemented by adifferent computing device (e.g., one of the client devices 110 a-110 n)or a separate server from the server(s) 102 hosting the visual designsystem 104. Indeed, as shown in FIG. 1 , the client devices 110 a-110 ninclude the digital asset recommendation system 106 (as well as theasset-recommendation-machine-learning model 114). Example components ofthe digital asset recommendation system 106 will be described below withregard to FIG. 7 .

As mentioned above, in one or more embodiments, the digital assetrecommendation system 106 generates one or more digital assets from adigital image. FIG. 2 illustrates an overview diagram of the digitalasset recommendation system 106 generating digital assets from a digitalimage in accordance with one or more embodiments.

In one or more embodiments, a digital asset includes a graphical objector a textual object. Such a digital asset can include, for example, adigital graphic, image, or icon, as well as digital text or digitalcharacters. In particular, in some embodiments, a digital asset includesa graphical object or a textual object that is used as a building blockfor a visual design. For instance, in some cases, a digital assetincludes a digital element that can be inserted into a visual design orotherwise applied to one or more other elements (e.g., objects) of thevisual design to affect their appearance. In one or more embodiments, adigital asset includes, but is not limited to, a shape asset, a patternasset, a color palette asset, a color gradient asset, a font asset, or afont theme asset. In some implementations, a digital asset includes aproduction-ready digital visual design element having a configurationapplied thereto (e.g., in contrast to a preprocessed digital assetdiscussed below).

In some cases, a shape asset includes a digital object. In particular,in some cases, a shape asset includes vector object, such as a scalablevector graphic (SVG) depicted in a digital image. In someimplementations, a shape asset includes a gray scale or black-and-whitevariation of a digital object depicted in a digital image.

In some implementations, a pattern asset includes a repetitive visualsequence. For instance, in some cases, a pattern asset includes arepetitive sequence of a portion of a digital image, such as one or moredigital objects portrayed in the digital image and/or the portion(s) ofthe digital image surrounding the digital object(s). In someembodiments, the portion of the digital image used in the pattern assetis arranged in a tile that consists of its own configuration havingmultiple instances of the portion of the digital image (e.g., multipleinstances of a digital object oriented or positioned differently withinthe tile). Thus, in one or more embodiments, the digital assetrecommendation system 106 utilizes an arrangement of a portion of adigital image to generate a tile and uses a repetitive pattern of thetile to create a pattern asset.

In some embodiments, a color palette asset includes a color theme or acolor selection. For instance, in some cases, a color palette assetincludes a selection of a subset of colors portrayed within a digitalimage (e.g., a color palette). To illustrate, in some implementations, acolor palette asset includes a selection of one or more colors portrayedwithin a foreground of a digital image. In some cases, however, a colorpalette includes one or more colors from a background of the digitalimage.

In one or more embodiments, a color gradient asset includes a gradientor variation of colors from dark to bright (or vice versa). Inparticular, in some embodiments, a color gradient asset includes agradient of the colors portrayed in at least a portion of a digitalimage. For example, in some embodiments, a color gradient asset includesa variation of the colors portrayed within a background of a digitalimage. In some cases, however, a color gradient asset includes agradient of the colors portrayed in the foreground of the digital image.

In some implementations, a font asset includes a particular design or aparticular style of a typeface for a collection of characters. Inparticular, in some implementations, a font asset includes a characterstyle (e.g., a font) associated with text depicted in a digital image. Afont can likewise include a combination of a typeface and otherstylistic qualities for a collection of characters, such as pitch,spacing, and size. In some cases, a font asset further includes the textassociated with the font. Relatedly, in one or more embodiments, a fonttheme asset includes a group of fonts. In particular, in someembodiments, a font theme asset includes a group of related fontsdepicted in a digital image.

As shown in FIG. 2 , the digital asset recommendation system 106determines (e.g., identifies, receives, or otherwise obtains) a digitalimage 202 for use in generating one or more digital assets. In one ormore embodiments, a digital image includes a digital visualrepresentation (e.g., an image composed of digital data). In particular,in some embodiments, a digital image includes a digital file that ismade of digital image data and is displayable via a graphical userinterface. For example, in some implementations, a digital imageincludes a digital photo, a digital rendering (e.g., a scan or otherdigital reproduction) of a photograph or other document, or a frame of adigital video or other animated sequence. In some implementations, adigital image includes a digitally generated drawing, chart, map, graph,logo, or other graphic.

In one or more embodiments, the digital asset recommendation system 106determines the digital image 202 by receiving the digital image 202 froma computing device (e.g., a server hosting a third-party system or aclient device). In some embodiments, however, the digital assetrecommendation system 106 determines the digital image 202 by accessinga database storing digital images. For example, in at least oneimplementation, the digital asset recommendation system 106 maintains adatabase and stores a plurality of digital images therein. In someinstances, an external device or system stores digital images for accessby the digital asset recommendation system 106.

In some embodiments, the digital asset recommendation system 106determines the digital image 202 by receiving an indication of thedigital image 202. For instance, in some cases, the digital assetrecommendation system 106 receives a storage location of the digitalimage 202, a file name of the digital image 202, or a selection of thedigital image 202. Accordingly, the digital asset recommendation system106 retrieves the digital image 202 based on the received indication. Toillustrate, in some instances, the digital asset recommendation system106 operates on a computing device (e.g., the server(s) 102 or one ofthe client devices 110 a-110 n discussed above with reference to FIG. 1or some other mobile computing device, such as a smart phone or tablet).Accordingly, in some embodiments, the digital asset recommendationsystem 106 retrieves the digital image 202 by accessing the digitalimage 202 from local storage or from a remote storage location that isaccessible to the computing device.

As shown in FIG. 2 , the digital asset recommendation system 106utilizes an asset-recommendation-machine-learning model 204 to analyzethe digital image 202. In one or more embodiments, a machine-learningmodel includes a computer representation that can be tuned (e.g.,trained) based on inputs to approximate unknown functions. Inparticular, in some embodiments, a machine-learning model includes amodel that utilizes algorithms to learn from, and make predictions on,known data by analyzing the known data to learn to generate outputs thatreflect patterns and attributes of the known data. For instance, in someimplementations, a machine-learning model includes, but is not limitedto a neural network (e.g., a convolutional neural network, recurrentneural network or other deep learning network), a decision tree (e.g., agradient boosted decision tree), association rule learning, inductivelogic programming, support vector learning, Bayesian network,regression-based model (e.g., censored regression), principal componentanalysis, or a combination thereof.

In one or more embodiments, an asset-recommendation-machine-learningmodel includes a machine-learning model that generates digital assetsfrom digital images. In particular, in some embodiments, anasset-recommendation-machine-learning model includes a machine-learningmodel that analyzes a digital image (e.g., analyzes features orcharacteristics of the digital image, such as colors, fonts, and/ordigital objects portrayed in a digital image) and generates one or morerecommended digital assets from the digital image. As will be discussedbelow, in some cases, an asset-recommendation-machine-learning modelincludes a machine-learning model that identifies digital asset classesthat are associated with a digital image, generates a one or moredigital assets that are from those digital asset classes, and selects atleast one of the digital assets to provide via a recommendation. Forinstance, the asset-recommendation-machine-learning model can includevarious different networks, such as one or moreasset-classification-neural networks, one or more pre-asset networks,and one or more asset-configuration-neural networks. In one or moreembodiments, an asset-recommendation-machine-learning mode includesvarious components (e.g., models) for analyzing a digital image andgenerating one or more digital assets accordingly.

Indeed, as shown in FIG. 2 , the digital asset recommendation system 106utilizes various components of the asset-recommendation-machine-learningmodel 204 to analyze the digital image 202. For instance, as shown, thedigital asset recommendation system 106 utilizes anasset-classification-neural network 206 to determine one or more digitalasset classes associated with the digital image 202. In particular, thedigital asset recommendation system 106 utilizes theasset-classification-neural network 206 to generate classificationmetrics 208 (e.g., within a string of labels) for the digital assetclasses based on the digital image 202.

To provide some context, in one or more embodiments, a neural networkincludes a machine learning model that includes a model ofinterconnected artificial neurons (e.g., organized in layers) thatcommunicate and learn to approximate complex functions and generateoutputs based on a plurality of inputs provided to the model. In someinstances, a neural network includes one or more machine learningalgorithms. Further, in some cases, a neural network comprises analgorithm (or set of algorithms) that implements deep learningtechniques that utilize a set of algorithms to model high-levelabstractions in data. To illustrate, in some embodiments, a neuralnetwork includes a convolutional neural network, a recurrent neuralnetwork (e.g., a long short-term memory neural network), a generativeadversarial neural network, a graph neural network, or a multi-layerperceptron. In some embodiments, a neural network includes a combinationof neural networks or neural network components.

In one or more embodiments, an asset-classification-neural networkincludes a computer-implemented neural network that determines digitalasset classes that are associated with a digital image. In particular,in some embodiments, an asset-classification-neural network includes aneural network that analyzes a digital image (e.g., analyzes patentand/or latent features of the digital image) and determines one or moredigital assets classes associated with the digital image based on theanalysis. For instance, in some cases, an asset-classification-neuralnetwork generates a classification metric corresponding to one or moredigital asset classes for the digital image. More detail regarding theasset-classification-neural network 206 will be provided below.

In one or more embodiments, a digital asset class includes aclassification of digital assets. In particular, in some cases, adigital asset class includes a label associated with digital assetshaving one or more common characteristics or attributes. For instance,in some implementations, a digital asset class includes, but is notlimited to, a shape asset class, a color asset class, a pattern assetclass, or a font asset class. In some cases, a digital asset class isassociated with multiple types of digital assets. For example, in one ormore embodiments, a color asset class is associated with color paletteassets and color gradient assets. As another example, in some cases, afont asset class is associated with font assets or font theme assets.

In one or more embodiments, a classification metric includes a measureof a relationship between a digital image and a corresponding digitalasset class. In particular, in some embodiments, a classification metricincludes a value that indicates the strength of the relationship betweena digital image and a corresponding digital asset class. For instance,in some cases, a classification metric includes a probability that adigital image is associated with a corresponding digital asset class. Insome cases, a classification metric includes a score value indicatinghow well the digital image and the corresponding digital asset classmatch. In one or more embodiments, the digital asset recommendationsystem 106 utilizes a classification metric corresponding to a digitalasset class to determine whether or not the digital image is a candidatefor use in generating a digital asset from that digital asset class.

In some implementations, the digital asset recommendation system 106utilizes the asset-classification-neural network 206 of theasset-recommendation-machine-learning model 204 to generateclassification metrics for the shape asset class, the color asset class,and the pattern asset class. In some cases, the digital assetrecommendation system 106 utilizes a separate model for generating aclassification metric for the font asset class as will be discussed inmore detail below. In some embodiments, however, the digital assetrecommendation system 106 utilizes the asset-classification-neuralnetwork 206 for generating the classification metric for the font assetclass as well.

As further shown in FIG. 2 , the digital asset recommendation system 106utilizes pre-asset networks 210 of theasset-recommendation-machine-learning model 204 to generate, from thedigital image 202, preprocessed digital assets 212 associated with thedigital asset classes. For instance, in some cases, the digital assetrecommendation system 106 utilizes the pre-asset networks 210 togenerate one or more preprocessed digital assets from a digital assetclass based on the classification metric for that digital asset classsatisfying a threshold.

In one or more embodiments, a pre-asset network includes acomputer-implemented model for generating preprocessed digital assets.In particular, in some embodiments, a pre-asset network includes acomputer-implemented model that analyzes a digital image, identifies oneor more areas of interest within the digital image, and generates one ormore preprocessed digital assets using the area(s) of interest. In somecases, a pre-asset network includes a machine-learning model, such as aneural network. In some implementations, a pre-asset network includes anon-machine learning, computer-implemented model. More detail regardingthe pre-asset networks 210 will be provided below.

In one or more embodiments, a preprocessed digital asset includes adigital asset that has been extracted, isolated, or segmented from adigital image. In particular, in some embodiments, a preprocesseddigital asset includes a raw graphical object or textual objectextracted, isolated, or segmented from a digital image before having aconfiguration applied thereto. For instance, in some cases, apreprocessed digital asset includes a graphical object or textual objectgenerated by a pre-asset network. To illustrate, in someimplementations, a preprocessed digital asset includes a preprocessedshape asset corresponding to a shape asset class or a pattern assetclass, or a preprocessed color asset corresponding to a color assetclass. In some cases, a preprocessed digital asset includes apreprocessed font asset or a preprocessed font theme asset correspondingto a font asset class; however, as will be shown in more detail below,the digital asset recommendation system 106 utilizes a pre-asset networkto generate a finalized (e.g., configured) font asset or font themeasset from the digital image in some embodiments.

Additionally, as shown in FIG. 2 , the digital asset recommendationsystem 106 utilizes asset-configuration-neural networks 214 of theasset-recommendation-machine-learning model 204 to generate digitalassets 216 using the preprocessed digital assets 212.

In one or more embodiments, an asset-configuration-neural networkincludes a computer-implemented neural network that determines aconfiguration for a digital asset. In particular, in some embodiments,an asset-configuration-neural network includes a neural network thatanalyzes a preprocessed digital asset, determines a configuration forthe preprocessed digital asset, and applies the configuration to thepreprocessed digital asset to produce a digital asset. For instance, insome implementations, an asset-configuration-neural network determines ablack-and-white or grayscale conversion for a digital object, anarrangement of a digital object within a tile, or a mood of a digitalimage for use in creating a color palette. To illustrate such networks,in some embodiments, an asset-configuration-neural network includes aneural network classifier. More detail regarding theasset-configuration-neural networks 214 will be discussed below.

As shown in FIG. 2 , the digital asset recommendation system 106 selectsrecommended digital assets 218 from among the digital asset 216. In oneor more embodiments, recommended digital asset includes a digital assetto be provided to a client device as part of a recommendation. Inparticular, in some cases, a recommended digital asset includes adigital asset from a digital image that is recommended for subsequentimplementation. For example, in some cases, the digital assetrecommendation system 106 selects a subset of the digital assets 216 foruse as the recommended digital assets 218 (though, in some cases, thedigital asset recommendation system 106 can use all of the digitalassets 216 for use as the recommended digital assets 218). As shown inFIG. 2 , in some implementations, the digital asset recommendationsystem 106 utilizes the asset-recommendation-machine-learning model 204to determine the recommended digital assets 218 from the digital assets216.

As further shown in FIG. 2 , the digital asset recommendation system 106provides the recommended digital assets 218 for display within agraphical user interface 220 of a client device 222. To illustrate, inone or more embodiments, the digital asset recommendation system 106detects one or more user interactions via the graphical user interface220 for generating a digital asset from the digital image 202 based onthe one or more user interactions. In response to detecting the userinteraction(s), the digital asset recommendation system 106 utilizes theasset-recommendation-machine-learning model 204 to generate the digitalassets 216 and determines the recommended digital assets 218. Upondetermining completion of the user-selected generation of the digitalasset via the user interactions, the digital asset recommendation system106 provides the recommended digital assets 218 for display within thegraphical user interface 220. For instance, in some cases, the digitalasset recommendation system 106 provides the recommended digital assets218 for display within a save screen of the graphical user interface 220for storing the manually generated digital asset. Thus, in someembodiments, the digital asset recommendation system 106 provides therecommended digital assets 218 for display along with the generateddigital asset, enabling the client device 222 to efficiently selectdigital assets to store from among the recommended digital assets 218while also storing the generated digital asset.

In some implementations, the digital asset recommendation system 106further provides options for editing the recommended digital assets 218via the graphical user interface 220. For instance, the digital assetrecommendation system 106 provides the recommended digital assets 218for display via the graphical user interface 220. In response todetecting a user selection of a recommended digital asset, the digitalasset recommendation system 106 provides a selectable option formodifying the recommended digital asset. Upon further detection of auser selection of the selectable option, the digital assetrecommendation system 106 provides one or more interactive elements formodifying the recommended digital asset via the graphical user interface220.

As previously mentioned, in one or more embodiments, the digital assetrecommendation system 106 utilizes anasset-recommendation-machine-learning model for generating digitalassets from a digital image and determining recommended digital assetsfor provision to a client device. FIG. 3 illustrates an architecture ofan asset-recommendation-machine-learning model in accordance with one ormore embodiments.

As shown in FIG. 3 , the asset-recommendation-machine-learning model 300utilized by the digital asset recommendation system 106 includes aclassification metric generator 302 for generating classificationmetrics. As illustrated by FIG. 3 , the classification metric generator302 includes an asset-classification-neural network 304. In one or moreembodiments, the classification metric generator 302 includes variousneural network layers, such as a convolutional layer, a depth-wiselayer, and a sigmoid layer. In some embodiments, theasset-classification-neural network 304 includes a MobileNetarchitecture, such as the MobileNet v1 architecture described by AndrewG. Howard and Menglong Zhu, MobileNets: Open-Source Models for EfficientOn-device Vision, Google AI Blog,https://ai.googleblog.com/2017/06/mobilenets-open-source-models-for.html(2017) or the MobileNet v2 architecture described by Mark Sandler andAndrew Howard, MobileNetV2: The Next Generation of On-device ComputerVision Networks, Google AI Blog,https://ai.googleblog.com/2018/04/mobilenetv2-next-generation-of-on.html(2018), both of which are incorporated herein by reference in theirentirety. In some implementations, the asset-classification-neuralnetwork 304 includes one of the residual neural network architecturesdescribed by Vincent Feng, An Overview of ResNet and its Variants,https://towardsdatascience.com/an-overview-of-resnet-and-its-variants-5281e2f56035(2017), which is incorporated herein by reference in its entirety.

As further shown, the classification metric generator 302 includes afont classification model 306. In one or more embodiments, a fontclassification model includes a computer-implemented model forgenerating a classification metric for a font asset class. Inparticular, in one or more embodiments, a font classification modelincludes a computer-implemented model that generates a classificationmetric for a font asset class based on text depicted in a digital image.To illustrate, in some cases, a font classification model generates aclassification metric for a font asset class based on the height andlength of the text depicted in the digital image. Indeed, in some cases,a font classification model generates a value for each of the text boxes(e.g., blocks of text) depicted in a digital image and combines thevalue determined for each text box to determine a classification metricfor the font asset class.

Thus, as shown in FIG. 3 , the digital asset recommendation system 106provides a digital image 308 to the classification metric generator 302of the asset-recommendation-machine-learning model 300. Further, thedigital asset recommendation system 106 utilizes the classificationmetric generator 302 to generate classification metrics 310 for thedigital asset classes. In particular, the digital asset recommendationsystem 106 utilizes the asset-classification-neural network 304 togenerate classification metrics for various digital asset classes—suchas a shape asset class 312, a color asset class 314, and a pattern assetclass 316—based on an analysis of the digital image 308. The digitalasset recommendation system 106 further utilizes the font classificationmodel 306 to generate a classification metric for a font asset class 318based on an analysis of the digital image 308.

As illustrated by FIG. 3 , the asset-recommendation-machine-learningmodel 300 also includes pre-asset networks 320. In particular, as shown,the pre-asset networks 320 includes an object-detection-neural network322. In one or more embodiments, an object-detection-neural networkincludes a computer-implemented neural network that detects salientfeatures in a digital image. In particular, in some embodiments, anobject-detection-neural network includes a neural network that detectsone or more digital objects portrayed in a digital image. To illustrate,in some embodiments, an object-detection-neural network identifies aportion (e.g., a region) of a digital image that includes a digitalobject. Indeed, in some instances, an object-detection-neural networkgenerates one or more bounding boxes from a digital image, where eachbounding box includes a portion of the digital image that includes adigital object. In some cases, an object-detection-neural networkincludes a region-based neural network, such as a region-basedconvolutional neural network. For instance, in one or more embodiments,the object-detection-neural network 322 includes one of the region-basedobject detectors described in Jonathan Hui, What Do We Learn From RegionBased Object Detectors (Faster R-CNN, R-FCN, FPN),https://jonathan-hui.medium.com/what-do-we-learn-from-region-based-object-detectors-faster-r-cnn-r-fcn-fpn-7e354377a7c9(2018), which is incorporated herein by reference in its entirety. Insome embodiments, the object-detection-neural network 322 includes thefaster region-based convolutional neural network (Faster R-CNN)described by Shaoqing Ren et al., Faster R-CNN: Towards Real-time ObjectDetection with Region Proposal Networks,https://arxiv.org/pdf/1506.01497.pdf (2016), which is incorporatedherein by reference.

In one or more embodiments, a digital object includes an item or objectportrayed in a digital image. In particular, in one or more embodiments,a digital object includes an organic or non-organic object depicted in adigital image. To illustrate, in some embodiments, a digital objectincludes, but is not limited to, a person, an animal, a building, aplant, a vehicle, a chair, or a handheld item.

As shown by FIG. 3 , the pre-asset networks 320 further includes anobject-extraction-neural network 324. In one or more embodiments, anobject-extraction neural network includes a computer implemented neuralnetwork that extracts a digital object from a digital image. Toillustrate, in some embodiments, an object-extraction-neural networkincludes a neural network that extracts a digital object from a portionof the digital image containing the digital object as identified by anobject-detection-neural network. Indeed, in some cases, anobject-extraction neural network utilizes a bounding box generated by anobject-detection-neural network to extract a digital object portrayedtherein. In some implementations, an object-extraction-neural networkgenerates a mask for the digital object portrayed in the digital image.In some cases, the object-extraction-neural network further applies themask to the digital image (e.g., to the bounding box) to extract theportrayed digital object. In one or more embodiments, anobject-extraction-neural network includes a mask region-based neuralnetwork, such as a mask region-based convolutional neural network.Indeed, in some implementations, the object-extraction-neural network324 includes the mask region convolutional neural network (Mask R-CNN)described by Elisha Odemakinde, Mask R-CNN: A Beginner's Guide,https://viso.ai/deep-learning/mask-r-cnn/(2021) or Heramb Devbhankar,Instance Segmentation with Mask R-CNN,https://towardsdatascience.com/instance-segmentation-with-mask-r-cnn-6e5c4132030b(2020), both of which are incorporated herein by reference in theirentirety. In some embodiments, the object-extraction-neural network 324includes the mask scoring regional convolutional neural network (MSR-CNN) described by Zhaojin Huang et al., Mask Scoring R-CNN,https://arxiv.org/pdf/1903.00241.pdf (2019), which is incorporatedherein by reference.

Additionally, as shown, the pre-asset networks 320 include aforeground-background-segmentation model 326. In one or moreembodiments, a foreground-background-segmentation model includes acomputer-implemented model that separates the foreground of a digitalimage from the background of the digital image. In particular, in someembodiments, a foreground-background-segmentation model includes acomputer-implemented model that implements various computer visionalgorithms to extract the foreground from a digital image (e.g., digitalobjects portrayed in the digital image or a foreground landscapeportrayed in the digital image). In some cases, aforeground-background-segmentation model generates a foreground imagelayer (e.g., an image that includes only a foreground) from theextracted foreground and generates a background image layer (e.g., animage that contains only a background) with the remaining background ofthe digital image. In one or more embodiments, theforeground-background-segmentation model includes one of the neuralnetworks described above with reference to the object-detection-neuralnetwork 322 and the object-extraction-neural network 324. In someembodiments, the foreground-background-segmentation model 326 includesone or more of the image segmentation models described by Derrick Mwitiand Katherine (Yi) Li, Image Segmentation in 2021: Architecture, Losses,Datasets, and Frameworks,https://neptune.ai/blog/image-segmentation-in-2020 (2021), which isincorporated herein by reference.

Further, as shown in FIG. 3 , the pre-asset networks 320 include a textextraction model 328. In one or more embodiments, a text extractionmodel includes a computer-implemented model that extracts one or morefonts (e.g., texts) from a digital image. In particular, in someembodiments, a text extraction model includes a model that identifiesand extracts one or more fonts based on a text height and/or text lengthof the text boxes associated with the font. For instance, in some cases,a text extraction model determines the text length and text height ofeach text box depicted in a digital image, determines a value for eachtext box based on their text length and text height, and selects one ormore fonts based on the values determined for their corresponding textboxes. To illustrate, in some cases, the text extraction model selectsone or more fonts associated with the highest values.

Thus, the digital asset recommendation system 106 utilizes the pre-assetnetworks 320 to generate preprocessed digital assets from the digitalimage 308. Indeed, in some cases, the digital asset recommendationsystem 106 provides the digital image 308 to the pre-asset networks 320and utilizes the pre-asset networks 320 to generate one or morepreprocessed digital assets for one or more digital asset classes. Insome cases, the digital asset recommendation system 106 utilizes one ofthe pre-asset networks 320 to generate a preprocessed digital asset fora digital asset class. In some implementations, however, the digitalasset recommendation system 106 utilizes multiple pre-asset networks togenerate a preprocessed digital asset. More detail regarding thepre-asset network(s) used in generating a particular preprocesseddigital asset will be discussed below with reference to FIGS. 4A-4E.

As illustrated by FIG. 3 , the asset-recommendation-machine-learningmodel 300 further includes asset-configuration-neural networks 330. Inparticular, the asset-configuration-neural networks 330 include ablack-and-white-pixel-classification-neural network 332. In one or moreembodiments, a black-and-white-pixel-classification-neural networkincludes a computer-implemented neural network that determines a valuefor converting an image to black-and-white or grayscale. In particular,in some embodiments, a black-and-white-pixel-classification-neuralnetwork includes a neural network that determines a value for generatinga black-and-white or grayscale object from a digital object portrayed ina digital image. For instance, in some cases, ablack-and-white-pixel-classification-neural network determines athreshold value for converting a particular pixel of a digital object toblack or white depending on a value (e.g., an RGB value) associated withthat pixel. In some cases, theblack-and-white-pixel-classification-neural network determines a rangeof values for converting pixels to grayscale.

As further illustrated by FIG. 3 , the asset-configuration-neuralnetworks 330 includes a tile-classification-neural network 334. In oneor more embodiments, a tile-classification-neural network includes acomputer-implemented neural network that determines a tile configurationusing a digital object extracted from a digital image. In particular, insome embodiments, a tile-classification-neural network includes a neuralnetwork that determines an arrangement of a digital object within atile. For instance, in some cases, a tile-classification-neural networkgenerates probabilities or other values for a pre-determined set of tilearrangements based on a digital object extracted from a digital image.

Additionally, as illustrated in FIG. 3 , the asset-configuration-neuralnetworks 330 include a color-mood-classification-neural network 336. Inone or more embodiments, a color-mood-classification-neural networkincludes a computer-implemented neural network that determines a colormood for a digital image. In particular, in some embodiments, acolor-mood-classification-neural network includes a neural network thatdetermines a color mood for a digital image based on a foreground of thedigital image. To illustrate, in some implementations, acolor-mood-classification-neural network generates probabilities orother values for a pre-determined set of color moods based on aforeground image layer generated from a digital image.

In one or more embodiments, theblack-and-white-pixel-classification-neural network 332, thetile-classification-neural network 334, and/or thecolor-mood-classification-neural network 336 include the same neuralnetwork architecture described above with reference to theasset-classification-neural network 304 (e.g., the MobileNet v1architecture, the MobileNet v2 architecture, or one of the residualneural network architectures described above).

Thus, in one or more embodiments, the digital asset recommendationsystem 106 provides the preprocessed digital assets generated by thepre-asset networks 320 to the asset-configuration-neural networks 330.Further, the digital asset recommendation system 106 utilizes theasset-configuration-neural networks 330 to generate digital assets fromthe preprocessed digital assets. More detail regarding theasset-configuration-neural network used in generating a particulardigital asset will be discussed below with reference to FIGS. 4A-4E.

As further shown in FIG. 3 , the asset-recommendation-machine-learningmodel 300 includes an asset ranking model 338. In one or moreembodiments, the asset ranking model 338 selects one or more digitalassets from among the digital assets generated by theasset-configuration-neural networks 330 for use as recommended digitalassets. In some cases, the asset ranking model 338 selects from thedigital assets based on an asset score associated with each digitalasset. In one or more embodiments, an asset score includes aquantitative value associated with a digital asset. In particular, insome embodiments, an asset score includes a numerical value thatindicates a quality of a digital asset or a relevance of the digitalasset to the digital image from which the digital asset was generated.

Indeed, in some embodiments, the asset ranking model 338 determines anasset score for each digital asset generated by theasset-configuration-neural networks 330, ranks the digital assets basedon their corresponding asset scores (e.g., by comparing their assetscores), and selects one or more digital assets for use as recommendeddigital assets based on the ranking. In one or more embodiments, theasset-recommendation-machine-learning model 300 generates a score valueto be associated with a digital asset at each stage of the generationprocess and determines the asset score for the digital asset bycombining the score values associated with that digital asset. Indeed,in one or more embodiments, a score value includes a quantitative valueassociated with a digital asset and generated at a particular stage ofthe digital asset generation process. To illustrate, in someembodiments, a score value includes a quantitative value associated witha digital asset as determined by the asset-classification-neural network304, at least one of the pre-asset networks 320, or one of theasset-configuration-neural networks 330. Thus, in some cases, an assetscore includes a combination of score values. More detail regardingdetermining the score values for digital assets will be provided belowwith reference to FIGS. 4A-4E.

Thus, the digital asset recommendation system 106 utilizes theasset-recommendation-machine-learning model 300 to determine arecommended digital asset 340 from a digital image. Though a singlerecommended digital asset is shown, in some implementations, the digitalasset recommendation system 106 utilizes theasset-recommendation-machine-learning model 300 to determine multiplerecommended digital assets from a digital image. As an example, thedigital asset recommendation system 106 can utilize theasset-recommendation-machine-learning model to generate a shape asset, apattern asset, and a color palette asset based on a single digitalimage, as will be discussed below with reference to FIG. 6 .

As mentioned above, the digital asset recommendation system 106 utilizesdifferent components of an asset-recommendation-machine-learning modelto generate different digital assets. In particular, the digital assetrecommendation system 106 utilizes a particular set of components of theasset-recommendation-machine-learning model to generate digital assetsof a particular digital asset class. FIGS. 4A-4E illustrate diagrams forutilizing components of an asset-recommendation-machine-learning model400 to generate various digital assets in accordance with one or moreembodiments. While a single version of theasset-recommendation-machine-learning model 400 can include variousasset-classification-neural networks, pre-asset networks, andasset-configuration-neural networks depicted in FIGS. 4A-4E, thefollowing paragraphs describe the digital asset recommendation system106 utilizing only a subset of such asset-classification-neuralnetworks, pre-asset networks, and asset-configuration-neural networkswhen generating particular digital assets. As suggested by FIGS. 4A-4Eand described further below, the digital asset recommendation system 106can intelligently detect a certain digital asset class from a digitalimage and utilize a select subset of such asset-classification-neuralnetworks, pre-asset networks, and asset-configuration-neural networks togenerate a corresponding digital asset based on the detected digitalasset class and various thresholds.

In accordance with one or more embodiments, FIG. 4A illustrates adiagram of the digital asset recommendation system 106 utilizing variouscomponents of the asset-recommendation-machine-learning model 400 togenerate a shape asset 402 corresponding to a shape asset class from adigital image 404.

Indeed, as shown in FIG. 4A, the digital asset recommendation system 106utilizes an asset-classification-neural network 406 of theasset-recommendation-machine-learning model 400 to generate aclassification metric 408 for a shape asset class 410 based on ananalysis of the digital image 404. In one or more embodiments, thedigital asset recommendation system 106 determines to use theclassification metric 408 as the score value for the shape asset 402from that stage of the generation process.

As further shown in FIG. 4A, the digital asset recommendation system 106utilizes an object-detection-neural network 412 and anobject-extraction-neural network 414 of theasset-recommendation-machine-learning model 400 to generate apreprocessed shape asset 416 from the digital image 404. In particular,in some cases, the object-detection-neural network 412 detects an objectportrayed in the digital image 404 by identifying a bounding box thatincludes the digital object, and the object-extraction-neural network414 extracts the digital object from the digital image 404 using theidentified bounding box. In one or more embodiments, the digital assetrecommendation system 106 determines to use the portion (e.g.,percentage) of the digital image 404 occupied by the identified boundingbox as a score value for the shape asset 402. In some cases, the digitalasset recommendation system 106 further utilizes a confidence scoregenerated by the object-extraction-neural network 414 in generatingand/or applying the mask for the digital object as another score valuefor the shape asset 402.

Additionally, as shown, the digital asset recommendation system 106utilizes a black-and-white-pixel-classification-neural network 418 ofthe asset-recommendation-machine-learning model 400 to generate theshape asset 402 from the preprocessed shape asset 416. In particular,the digital asset recommendation system 106 utilizes theblack-and-white-pixel-classification-neural network 418 to determine athreshold value for converting the extracted digital object toblack-and-white (or ranges of values for converting the digital objectto grayscale). The digital asset recommendation system 106 furtherapplies the threshold value (or ranges of values) to the preprocessedshape asset 416 (e.g., the extracted digital object) to generate ablack-and-white object (or grayscale object). In one or moreembodiments, the digital asset recommendation system 106 determines touse the threshold value determined by theblack-and-white-pixel-classification-neural network 418 as the scorevalue for the shape asset 402 at that stage of the generation process.

Thus, in one or more embodiments, the digital asset recommendationsystem 106 generates the shape asset 402 from the digital image 404 bygenerating a black-and-white or grayscale object (e.g., shape vector)using a digital object depicted in the digital image 404. Further, inone or more embodiments, the digital asset recommendation system 106determines an asset score for the shape asset 402 by combining the scorevalues determined from the asset-classification-neural network 406, theobject-detection-neural network 412, the object-extraction-neuralnetwork 414, and/or the black-and-white-pixel-classification-neuralnetwork 418. In some instances, the digital asset recommendation system106 normalizes or applies weights to the score values before combiningthem to determine the asset score.

FIG. 4B illustrates a diagram of the digital asset recommendation system106 utilizing various components of theasset-recommendation-machine-learning model 400 to generate a patternasset 422 corresponding to a pattern asset class from a digital image424 in accordance with one or more embodiments.

As shown in FIG. 4B, the digital asset recommendation system 106utilizes an asset-classification-neural network 406 of theasset-recommendation-machine-learning model 400 to generate aclassification metric 428 for a pattern asset class 430 based on ananalysis of the digital image 424. In one or more embodiments, thedigital asset recommendation system 106 determines to use theclassification metric 428 as the score value for the pattern asset 422from that stage of the generation process.

As further shown in FIG. 4B, and as discussed above with reference toFIG. 4A, the digital asset recommendation system 106 utilizes anobject-detection-neural network 432 and an object-extraction-neuralnetwork 434 of the asset-recommendation-machine-learning model 400 togenerate a preprocessed shape asset 436 from the digital image 424.Further, as discussed above with reference to FIG. 4A, the digital assetrecommendation system 106 determines to use—as score values for thepattern asset 422—the portion of the digital image 424 occupied by theidentified bounding box including the extracted digital object and/orthe confidence score in generating and/or applying a mask for thedigital object.

Additionally, as shown in FIG. 4B, the digital asset recommendationsystem 106 utilizes a tile-classification-neural network 438 of theasset-recommendation-machine-learning model 400 to generate the patternasset 422 from the preprocessed shape asset 436. In particular, thedigital asset recommendation system 106 utilizes the preprocessed shapeasset 436 to determine an arrangement of the extracted digital objectwithin a tile by, for example, generating probabilities or other valuesfor a pre-determined set of tile arrangements. The digital assetrecommendation system 106 further generates a tile having the determinedarrangement (e.g., the tile arrangement having the highest probabilityor one of the highest probabilities when generating multiple patternassets) and generates a pattern using a repetitive sequence of the tile.In one or more embodiments, the digital asset recommendation system 106determines to use the probability or other score value generated by thetile-classification-neural network 438 for the particular tilearrangement as the score value for the pattern asset 422 at that stageof the generation process.

Thus, in one or more embodiments, the digital asset recommendationsystem 106 generates the pattern asset 422 from the digital image 424 bygenerating a pattern of tile arrangements configured using a digitalobject depicted in the digital image 424. Further, in one or moreembodiments, the digital asset recommendation system 106 determines anasset score for the pattern asset 422 by combining the score valuesdetermined from the asset-classification-neural network 406, theobject-detection-neural network 432, the object-extraction-neuralnetwork 434, and/or the tile-classification-neural network 438. In someinstances, the digital asset recommendation system 106 normalizes orapplies weights to the score values before combining them to determinethe asset score.

FIG. 4C illustrates a diagram of the digital asset recommendation system106 utilizing various components of theasset-recommendation-machine-learning model 400 to generate a colorpalette asset 442 corresponding to a color asset class from a digitalimage 444 in accordance with one or more embodiments.

Indeed, as shown in FIG. 4C, the digital asset recommendation system 106utilizes an asset-classification-neural network 406 of theasset-recommendation-machine-learning model 400 to generate aclassification metric 448 for a color asset class 450 based on ananalysis of the digital image 444. In one or more embodiments, thedigital asset recommendation system 106 determines to use theclassification metric 448 as the score value for the color palette asset442 from that stage of the generation process.

As further shown in FIG. 4C, the digital asset recommendation system 106utilizes a foreground-background-segmentation model 452 of theasset-recommendation-machine-learning model 400 to generate apreprocessed color asset 454 from the digital image 444. In particular,in some embodiments, the digital asset recommendation system 106utilizes the foreground-background-segmentation model 452 to generatethe preprocessed color asset 454 corresponding to the color paletteasset 442 by generating a foreground image layer from the digital image444. Indeed, as discussed above, in one or more embodiments, theforeground-background-segmentation model 452 extracts the foregroundfrom the digital image 444 (e.g., extracts objects depicted in thedigital image and/or other foreground elements) and uses the extractedforeground as a foreground image layer. In one or more embodiments, thedigital asset recommendation system 106 determines a score for theforeground image layer (e.g., based on a portion of the digital image444 occupied by the foreground image layer) and utilizes the score as ascore value for the color palette asset 442.

As further shown in FIG. 4C, the digital asset recommendation system 106utilizes a color-mood-classification-neural network 456 of theasset-recommendation-machine-learning model 400 to generate the colorpalette asset 442 from the preprocessed color asset 454. In particular,the digital asset recommendation system 106 utilizes thecolor-mood-classification-neural network 456 to determine a color moodof the digital image 444 by, for example, generating probabilities orother values for a pre-determined set of color moods (e.g., colorful,bright, dark, muted, deep) based on the foreground image layer. Thedigital asset recommendation system 106 further generates a colorpalette corresponding to the determined color mood (e.g., the color moodhaving the highest probability or one of the highest probabilities whengenerating multiple color palette assets) using colors depicted in theforeground image layer. For instance, in some cases, the digital assetrecommendation system 106 utilizes a mapping of colors to color moods toidentify one or more of the colors included in the foreground imagelayer that correspond to the determined color mood. The digital assetrecommendation system 106 generates a color palette using those colors.In one or more embodiments, the digital asset recommendation system 106determines to use the probability or other score value generated by thecolor-mood-classification-neural network 456 for the particular colormood as the score value for the color palette asset 442 at that stage ofthe generation process.

Thus, in one or more embodiments, the digital asset recommendationsystem 106 generates the color palette asset 442 from the digital image444 by generating a selection of colors chosen from the foreground ofthe digital image 444. Further, in one or more embodiments, the digitalasset recommendation system 106 determines an asset score for the colorpalette asset 442 by combining the score values determined from theasset-classification-neural network 406, theforeground-background-segmentation model 452, and/or thecolor-mood-classification-neural network 456. In some instances, thedigital asset recommendation system 106 normalizes or applies weights tothe score values before combining them to determine the asset score.

In accordance with one or more embodiments, FIG. 4D illustrates adiagram of the digital asset recommendation system 106 using variouscomponents of the asset-recommendation-machine-learning model 400 togenerate a color gradient asset 462 corresponding to a color asset classfrom a digital image 464.

Indeed, as shown in FIG. 4D, the digital asset recommendation system 106utilizes an asset-classification-neural network 406 of theasset-recommendation-machine-learning model 400 to generate aclassification metric 468 for a color asset class 470 based on ananalysis of the digital image 464. In one or more embodiments, thedigital asset recommendation system 106 determines to use theclassification metric 468 as the score value for the color gradientasset 462 from that stage of the generation process.

As further shown in FIG. 4D, the digital asset recommendation system 106utilizes a foreground-background-segmentation model 472 of theasset-recommendation-machine-learning model 400 to generate apreprocessed color asset 474 from the digital image 464. In particular,in some embodiments, the digital asset recommendation system 106utilizes the foreground-background-segmentation model 472 to generatethe preprocessed color asset 474 corresponding to the color gradientasset 462 by generating a background image layer from the digital image464. Indeed, as discussed above, in one or more embodiments, theforeground-background-segmentation model 472 extracts the foregroundfrom the digital image 464 (e.g., extracts objects depicted in thedigital image and/or other foreground elements) and uses the remainingbackground as a background image layer. In one or more embodiments, thedigital asset recommendation system 106 determines a score for thebackground image layer (e.g., based on a portion of the digital image464 occupied by the background image layer) and utilizes the score as ascore value for the color gradient asset 462.

As further shown in FIG. 4D, the digital asset recommendation system 106generates the color gradient asset 462 from the preprocessed color asset474. Indeed, in one or more embodiments, the digital assetrecommendation system 106 extracts the colors from the background imagelayer and arranges the colors to form a color gradient. The digitalasset recommendation system 106 can arrange the colors from dark tolight, light to dark, or otherwise using the light color spectrum. Inone or more embodiments, the digital asset recommendation system 106determines a value score for the color gradient asset 462 based on theresulting color gradient. For instance, in some cases, the digital assetrecommendation system 106 determines a value score based on the range ofcolor represented in the color gradient or the smoothness of thetransition of color represented in the background image layer.

Thus, in one or more embodiments, the digital asset recommendationsystem 106 generates the color gradient asset 462 from the digital image464 by generating a gradient of colors chosen from the background of thedigital image 464. Further, in one or more embodiments, the digitalasset recommendation system 106 determines an asset score for the colorgradient asset 462 by combining the score values determined from theasset-classification-neural network 406, theforeground-background-segmentation model 472, and/or the resulting colorgradient. In some instances, the digital asset recommendation system 106normalizes or applies weights to the score values before combining themto determine the asset score.

In accordance with one or more embodiments, FIG. 4E illustrates adiagram of the digital asset recommendation system 106 using variouscomponents of the asset-recommendation-machine-learning model 400 togenerate a font asset 482 or a font theme asset 484 corresponding to acolor asset class from a digital image 486.

Indeed, as shown in FIG. 4E, the digital asset recommendation system 106utilizes a font classification model 488 of theasset-recommendation-machine-learning model 400 to generate aclassification metric 490 for a font asset class 492 based on ananalysis of the digital image 486. For example, in some embodiments, thefont classification model 488 determines the classification metric 490based on the text heights and text lengths of the text boxes depicted inthe digital image 486. In particular, in some cases, the fontclassification model 488 determines the classification metric 490 forthe digital image 486 as a whole based on a combination of the textheights and text lengths of the text boxes depicted in the digital image486. In one or more embodiments, the digital asset recommendation system106 determines to use the classification metric 490 as the score valuefor the font asset 482 or the font theme asset 484 from that stage ofthe generation process. In one or more embodiments, the fontclassification model 488 identifies and analyzes the various texts asdescribed in U.S. patent application Ser. No. 16/675,529 filed on Nov.6, 2019, entitled DETECTING TYPOGRAPHY ELEMENTS FROM OUTLINES, which isincorporated herein by reference in its entirety.

As further shown in FIG. 4E, the digital asset recommendation system 106utilizes a text extraction model 494 of theasset-recommendation-machine-learning model 400 to generate the fontasset 482 from the digital image 486. For instance, in one or moreembodiments, the text extraction model 494 generates the font asset 482by determining a score for each font represented in the digital image486 based on the text heights and text lengths of the text boxesassociated with the font. The text extraction model 494 generates thefont asset 482 using the font having the highest score (or one of thehighest scores when generating multiple font assets). In some cases, thetext extraction model 494 generates the font asset 482 by extracting thetext associated with the font or by identifying the font styleassociated with the font and creating the font asset 482 using the fontstyle. In one or more embodiments, the digital asset recommendationsystem 106 determines to use the score determined for the font as thescore value for the font asset 482 at that stage of the generationprocess.

Additionally, as shown in FIG. 4E, the digital asset recommendationsystem 106 utilizes the text extraction model 494 to generate the fonttheme asset 484 from the digital image 486. For instance, in one or moreembodiments, the text extraction model 494 generates the font themeasset 484 by scoring each font represented in the digital image 486 asdiscussed above. In some cases, the text extraction model 494 furtheridentifies related fonts and groups them into a font theme. In somecases, the text extraction model 494 determines a score for each fonttheme based on the individual scores of the included fonts. The textextraction model 494 generates the font theme asset 484 using the fonttheme having the highest score (or one of the highest scores whengenerating multiple font theme assets). In one or more embodiments, thedigital asset recommendation system 106 determines to use the scoredetermined for the font theme as the score value for the font themeasset 484 at that stage of the generation process.

Thus, in one or more embodiments, the digital asset recommendationsystem 106 generates the font asset 482 or the font theme asset 484using text depicted in the digital image 486. Further, in one or moreembodiments, the digital asset recommendation system 106 determines anasset score for the font asset 482 or the font theme asset 484 based onthe score values determined from the font classification model 488and/or the text extraction model 494 (e.g., based on a combination ofthe scores values). In some instances, the digital asset recommendationsystem 106 normalizes or applies weights to the score values beforecombining them to determine the asset score. It should be noted that,while FIG. 4E shows the digital asset recommendation system 106generating a font asset and a font theme asset from a digital image, thedigital asset recommendation system 106 can generate one or the other insome embodiments.

Accordingly, the digital asset recommendation system 106 utilizes theasset-recommendation-machine-learning model 400 to generate variousdigital assets from digital images. Indeed, in some cases, the digitalasset recommendation system 106 utilizes theasset-recommendation-machine-learning model to generate multiple digitalassets from a single digital image. In one or more embodiments, theasset-recommendation-machine-learning model further determines an assetscore for each digital asset generated from a digital image, ranks thedigital assets based on their corresponding asset score, and uses theranking to select one or more of the digital assets for provision to aclient device via recommendations. In one or more embodiments, thedigital asset recommendation system 106 normalizes or applies weights tothe asset scores and ranks the digital assets based on thenormalized/weighted scores.

The algorithm presented below represents another characterization of howthe digital asset recommendation system 106 utilizes anasset-recommendation-machine-learning model to generate one or moredigital assets from a digital image.

Algorithm Begin predictions = keras.applications.mobilenet.MobileNet().predict(input_image) // [predictions = [[‘Shape’, 0.70]], [‘Color’,0.25], [‘Pattern’, 0.05]]] ranking_queue<CCAsset, Rank>. // Listmaintains top ranked asset Type forEachTopPrediction{ assetType →  isshape?   begin:    detect_objects( ) → extract_objects( )→getRank( ) →vectorize_objects ( ) →    ranking_queue.add(Vectorized Shape, Rank)  end:  is pattern?   begin:    detect_objects( ) →     extract_objects() → forEach Object →      begin:       predictions =mobile.predict(Object) //Predict Tile Type       [prediction_result =[[Tile1, 0.70], [Tile2, 0.25], [Tile3, 0.05]]]       //Top PredictionTile 1       begin:       generate_pattern(Object, Tile1) //Createpattern of object and tile       type       getRank( ) →ranking_queue.add(Generate_Pattern, Rank)       end:      end:  iscolor?   begin:    background/foreground_segmentation( ) →     isdominant foreground?      extract_foreground( ) → getRank( ) →      ranking_queue.add(create_colorThme_of_foreground( ), rank)     isdominant background?      extract_background( ) →      create_gradient_of_background( ) → getRank( ) →      ranking_queue.add(create_gradient_of_background( ), rank)   end: is font?   begin:     OCR( ) → recognizeFontTypes( ) → getRank( ) →Generate Font Theme   end:  begin:  rankingQueue.getTopRankedAssets( ) →Recommend/Create/Save Top CC Assets  end: End

As mentioned above, in one or more embodiments, the digital assetrecommendation system 106 implements one or more thresholds fordetermining whether to move forward with generating a particular digitalasset or digital assets of a particular digital asset class. Forinstance, in some cases, the digital asset recommendation system 106implements a threshold at each stage of the generation process. Toillustrate, in some embodiments, the digital asset recommendation system106 implements a threshold at the classification metric generator stage,the pre-asset network stage, and/or the asset-configuration-neuralnetwork stage. In some instance, upon determining that a value (e.g., ascore value) for a digital asset fails to satisfy a correspondingthreshold, the digital asset recommendation system 106 determines toterminate generation of the digital asset. The thresholds used can bethe same or different for digital assets of different digital assettypes. To provide one example, in one or more embodiments, the digitalasset recommendation system 106 determines to move forward withgenerating a digital asset of a particular digital asset class only ifthe classification metric returned for that digital asset class exceeds0.50 (e.g., indicating that it is more likely than not that the digitalimage can be used to generate a digital asset of that digital assetclass).

Further, in one or more embodiments, various portions of the generationprocess implemented by the digital asset recommendation system 106 areconfigurable. Indeed, in some embodiments, the digital assetrecommendation system 106 modifies various portions of the generationprocess based on user input. For instance, the digital assetrecommendation system 106 can configure, based on user input, one ormore of the thresholds implemented, the number of digital assetsselected for recommendation, the number of digital assets of aparticular digital asset class considered for recommendation, or otheraspects of the digital assets that are generated (e.g., the number ofcolors used in a color palette asset).

Thus, the digital asset recommendation system 106 provides anunconventional approach that utilizes machine learning to generatedigital assets from digital images. Indeed, the digital assetrecommendation system 106 implements an unconventional orderedcombination of actions by incorporating various computer-implementedmodels within a machine learning framework (e.g., anasset-recommendation-machine-learning model) that analyzes a digitalimage and generates one or more digital assets based on the analysis.Thus, the digital asset recommendation system 106 utilizes machinelearning to provide a client device with recommendationsproduction-ready, pre-generated digital assets.

By incorporating the machine learning framework to generate digitalassets from digital images, the digital asset recommendation system 106further offers improved efficiency when compared to conventionalsystems. In particular, the digital asset recommendation system 106requires fewer user interactions with a graphical user interface togenerate production-ready digital assets from a digital image. Indeed,as discussed above, the digital asset recommendation system 106 utilizesthe machine learning framework to provide one or more recommendeddigital assets for display on a client device, allowing a user to viewpre-generated digital assets after uploading or selecting a digitalimage. Thus, the digital asset recommendation system 106 allows theclient device to store the recommended digital assets without requiringthe user interactions that are typically required under conventionalsystems to generate the digital assets.

In one or more embodiments, the digital asset recommendation system 106generates (e.g., trains or otherwise learns parameters for) anasset-recommendation-machine-learning model to generate digital assetsfrom digital images. In particular, the digital asset recommendationsystem 106 trains various components of theasset-recommendation-machine-learning model. FIG. 5 illustrates adiagram for training an asset-recommendation-machine-learning model inaccordance with one or more embodiments.

As shown in FIG. 5 , the digital asset recommendation system 106utilizes training data 502 to generate anasset-recommendation-machine-learning model 500. In particular, thedigital asset recommendation system 106 utilizes the training data 502to train an asset-classification-neural network 504 andasset-configuration-neural networks 506 of theasset-recommendation-machine-learning model 500.

In one or more embodiments, the training data 502 includes digitalimages previously utilized by users to generate at least one digitalasset. In some cases, the training data 502 further includes the digitalassets that were generated from those digital images and the parametersused for those digital assets. In some implementations, the trainingdata 502 further includes various mappings that map the digital imagesto the digital assets and/or their corresponding parameters. Forinstance, in some cases, the training data 502 includes a user-taggeddataset from Adobe Capture Service that includes digital images,resulting digital assets, and parameters used for those digital assetsas annotated by the users manually creating the digital assets.

In one or more embodiments, the digital asset recommendation system 106trains the asset-classification-neural network 504 and theasset-configuration-neural networks 506 utilizing the training data 502.In particular, the digital asset recommendation system 106 determinesthe weights to use for the asset-classification-neural network 504 andthe asset-configuration-neural networks 506 using the training data 502.For instance, in one or more embodiments, the digital assetrecommendation system 106 trains the asset-classification-neural network504 and the asset-configuration-neural networks 506 by adjusting theirweights to correctly classify a digital image or generate acorresponding digital asset, respectively, based on the training data502.

To illustrate, in one or more embodiments, the training data 502includes one or more mappings that map digital images to digital assetclasses. Indeed, the one or more mappings can indicate which digitalimages were used to create digital assets of a given digital assetclass. Thus, in one or more embodiments, the digital assetrecommendation system 106 trains the asset-classification-neural network504 using these mappings.

As another example, in some cases, the training data 502 includes one ormore mappings that map digital images used to create shape assets tothreshold values used for converting digital objects from the digitalimages to black-and-white (or grayscale). Indeed, the one or moremappings can indicate which threshold value was used for a given digitalimage (or for a given digital object portrayed in a digital image).Thus, in one or more embodiments, the digital asset recommendationsystem 106 trains a black-and-white-pixel-classification-neural network508 of the asset-configuration-neural networks 506 using these mappings.

Additionally, in one or more embodiments, the training data 502 includesone or more mappings that map digital images used to create patternassets to tile arrangements used for creating patterns from the digitalimages. Indeed, the one or more mappings can indicate which tilearrangement was used for a given digital image (or for a given digitalobject portrayed in a digital image). Thus, in one or more embodiments,the digital asset recommendation system 106 trains atile-classification-neural network 510 of the asset-configuration-neuralnetworks 506 using these mappings.

Further, in one or more embodiments, the training data includes one ormore mappings that map digital images used to create color paletteassets to color moods used for creating color palettes from the digitalimages. Indeed, the one or more mappings can indicate which color moodwas selected for creating a color palette from a given digital image.Thus, in one or more embodiments, the digital asset recommendationsystem 106 trains a color-mood-classification-neural network 512 of theasset-configuration-neural networks 506 using these mappings.

In one or more embodiments, the digital asset recommendation system 106trains the asset-classification-neural network 504 and theasset-configuration-neural networks 506 via transfer learning. Indeed,in one or more embodiments, the digital asset recommendation system 106leverages one or more pre-trained neural networks (e.g., trained on adifferent domain or set of training data) to learn parameters forimplementation via the asset-recommendation-machine-learning model 500.Thus, the digital asset recommendation system 106 can more efficientlytrain the asset-classification-neural network 504 and theasset-configuration-neural networks 506 for use in generating digitalassets from digital images.

FIG. 6 illustrates example digital assets generated from various digitalimages using an asset-recommendation-machine-learning model inaccordance with one or more embodiments. For example, as shown in FIG. 6, the digital asset recommendation system 106 passes a digital image 602through an asset-recommendation-machine-learning model 600 to generate ashape asset 604, a pattern asset 606, and a color palette asset 608. Inparticular, the digital asset recommendation system 106 analyzes thedigital object (e.g., the lion) depicted in the digital image 602 usingthe asset-recommendation-machine-learning model 600 to generate theshape asset 604, the pattern asset 606, and the color palette asset 608.

Additionally, as shown, the digital asset recommendation system 106passes a digital image 610 through theasset-recommendation-machine-learning model 600 to generate a colorgradient asset 612. In particular, the digital asset recommendationsystem 106 analyzes the background portrayed in the digital image 610using the asset-recommendation-machine-learning model 600 to generatethe color gradient asset 612.

Further, the digital asset recommendation system 106 passes a digitalimage 614 through the asset-recommendation-machine-learning model 600 togenerate a font asset 616. In particular, the digital assetrecommendation system 106 analyzes the text depicted in the digitalimage 614 using the asset-recommendation-machine-learning model 600 togenerate the font asset 616. In one or more embodiments, the digitalasset recommendation system 106 similarly generates a font theme asset(not shown) from the digital image 614. In some cases, the digital assetrecommendation system 106 passes a digital image depicting multipledifferent fonts through using the asset-recommendation-machine-learningmodel 600 to generate a font theme asset.

Thus, as indicated by FIG. 6 , the digital asset recommendation system106 can generate various numbers of digital assets from digital imagesusing the asset-recommendation-machine-learning model 600. Further, thedigital asset recommendation system 106 can generate digital assets ofvarious types from a single digital image. Indeed, the digital assetrecommendation system 106 provides efficient digital asset generation bygenerating one or more digital assets from a digital asset withoutreceiving user interactions for generating those digital assets.

In one or more embodiments, rather than directly generating a digitalasset from a digital image, the digital asset recommendation system 106provides one or more interactive elements (e.g., for display on agraphical user interface) for manually generating a digital asset. Toillustrate, in one or more embodiments, the digital asset recommendationsystem 106 identifies a digital image, such as a digital image that hasbeen uploaded to or otherwise accessed by the implementing computingdevice. Further, the digital asset recommendation system 106 determinesthat a digital asset class is associated with the digital image (e.g.,using an asset-classification-neural network of anasset-recommendation-machine-learning model).

In response, to identifying the digital asset class, the digital assetrecommendation system 106 provides one or more interactive elements thatcan be used for manually generating a digital asset associated with thatdigital asset class from the digital image. As one example, the digitalasset recommendation system 106 can provide one or more interactiveelements for creating a color palette asset from a digital image, suchas interactive elements for selecting a color mood, manually selectingindividual colors, or for modifying the brightness or RGB values of eachcolor. Thus, the digital asset recommendation system 106 can efficientlydirect a computing device to a module having tools for generating adigital asset of a given digital asset class upon detecting that such adigital asset class is associated with a digital image.

Turning to FIG. 7 , additional detail will now be provided regardingvarious components and capabilities of the digital asset recommendationsystem 106. In particular, FIG. 7 illustrates the digital assetrecommendation system 106 implemented by a computing device 700 (e.g.,the server(s) 102 and/or one of the client devices 110 a-110 n discussedabove with reference to FIG. 1 ). Additionally, the digital assetrecommendation system 106 is also part of the visual design system 104.As shown, in one or more embodiments, the digital asset recommendationsystem 106 includes, but is not limited to, a machine learning modeltraining engine 702, a machine learning model application manager 704, agraphical user interface manager 706, and data storage 708 (whichincludes an asset-recommendation-machine-learning model 710, trainingdata 712, and digital assets 714).

As just mentioned, and as illustrated in FIG. 7 , the digital assetrecommendation system 106 includes the machine learning model trainingengine 702. In one or more embodiments, the machine learning modeltraining engine 702 trains an asset-recommendation-machine-learningmodel to generate digital assets from digital images and provide some ofthe digital assets for recommendation. In particular, in some cases, themachine learning model training engine 702 trains anasset-classification-neural network and asset-configuration-neuralnetworks of the asset-recommendation-machine-learning model. Forinstance, in some implementations, the machine learning model trainingengine 702 utilizes training data to determine weights for theasset-classification-neural network and the asset-configuration-neuralnetworks.

Further, as shown in FIG. 7 , the digital asset recommendation system106 includes the machine learning model application manager 704. In oneor more embodiments, the machine learning model application manager 704utilizes the asset-recommendation-machine-learning model trained by themachine learning model training engine 702 to generate digital assetsfrom digital images. For instance, in some cases, the machine learningmodel application manager 704 utilizes theasset-recommendation-machine-learning model to analyze a digital image,determine one or more digital asset classes associated with the digitalimage, generate preprocessed digital assets corresponding to thosedigital asset classes, and generate digital assets from the preprocesseddigital assts. In some cases, the machine learning model applicationmanager 704 further utilizes the asset-recommendation-machine-learningmodel to select one or more of the generated digital assets to determinea set of recommended digital assets.

Additionally, as shown in FIG. 7 , the digital asset recommendationsystem 106 includes the graphical user interface manager 706. In one ormore embodiments, the graphical user interface manager 706 providesrecommended digital assets for display within a graphical userinterface. In some cases, the graphical user interface manager 706further detects user interactions for selecting and/or storing one ormore of the recommended digital assets. In some implementations, thegraphical user interface manager 706 detects one or more userinteractions for modifying one of the recommended digital assets andprovides interactive elements for modifying the selected recommendeddigital asset in response.

As shown in FIG. 7 , the digital asset recommendation system 106 furtherincludes data storage 708. In particular, data storage 708 includes theasset-recommendation-machine-learning model 710, training data 712, anddigital assets 714. In one or more embodiments, theasset-recommendation-machine-learning model 710 stores theasset-recommendation-machine-learning model trained by the machinelearning model training engine 702 and implemented by the machinelearning model application manager 704 to generate and recommend digitalassets from digital images. In one or more embodiments, training data712 stores the training data (e.g., the training digital images andmappings between the training digital images and generated digitalassets) utilized by the machine learning model training engine 702 totrain an asset-recommendation-machine-learning model. Further, in someembodiments, the digital assets 714 stores digital assets. For example,the digital assets 714 stores digital assets manually created by a userand/or digital assets generated from digital images using anasset-recommendation-machine-learning model.

Each of the components 702-714 of the digital asset recommendationsystem 106 can include software, hardware, or both. For example, thecomponents 702-714 can include one or more instructions stored on acomputer-readable storage medium and executable by processors of one ormore computing devices, such as a client device or server device. Whenexecuted by the one or more processors, the computer-executableinstructions of the digital asset recommendation system 106 can causethe computing device(s) to perform the methods described herein.Alternatively, the components 702-714 can include hardware, such as aspecial-purpose processing device to perform a certain function or groupof functions. Alternatively, the components 702-714 of the digital assetrecommendation system 106 can include a combination ofcomputer-executable instructions and hardware.

Furthermore, the components 702-714 of the digital asset recommendationsystem 106 may, for example, be implemented as one or more operatingsystems, as one or more stand-alone applications, as one or more modulesof an application, as one or more plug-ins, as one or more libraryfunctions or functions that may be called by other applications, and/oras a cloud-computing model. Thus, the components 702-714 of the digitalasset recommendation system 106 may be implemented as a stand-aloneapplication, such as a desktop or mobile application. Furthermore, thecomponents 702-714 of the digital asset recommendation system 106 may beimplemented as one or more web-based applications hosted on a remoteserver. Alternatively, or additionally, the components 702-714 of thedigital asset recommendation system 106 may be implemented in a suite ofmobile device applications or “apps.” For example, in one or moreembodiments, the digital asset recommendation system 106 can comprise oroperate in connection with digital software applications such as ADOBE®CAPTURE, ADOBE® ILLUSTRATOR®, or ADOBE® PHOTOSHOP®. The foregoing areeither registered trademarks or trademarks of Adobe Inc. in the UnitedStates and/or other countries.

FIGS. 1-7 , the corresponding text, and the examples provide a number ofdifferent methods, systems, devices, and non-transitorycomputer-readable media of the digital asset recommendation system 106.In addition to the foregoing, one or more embodiments can also bedescribed in terms of flowcharts comprising acts for accomplishing theparticular result, as shown in FIG. 8 . FIG. 8 may be performed withmore or fewer acts. Further, the acts may be performed in differentorders. Additionally, the acts described herein may be repeated orperformed in parallel with one another or in parallel with differentinstances of the same or similar acts.

FIG. 8 illustrates a flowchart of a series of acts 800 for generating adigital asset for recommendation from a digital image in accordance withone or more embodiments. While FIG. 8 illustrates acts according to oneembodiment, alternative embodiments may omit, add to, reorder, and/ormodify any of the acts shown in FIG. 8 . In some implementations, theacts of FIG. 8 are performed as part of a method. For example, in someembodiments, the acts of FIG. 8 are performed, in a digital mediumenvironment for digital design, as part of a computer-implemented methodfor generating recommended digital assets. Alternatively, anon-transitory computer-readable medium can store instructions thereonthat, when executed by at least one processor, cause a computing deviceto perform the acts of FIG. 8 . In some embodiments, a system performsthe acts of FIG. 8 . For example, in one or more embodiments, a systemincludes at least one memory device comprising anasset-recommendation-machine-learning model comprising anasset-classification-neural network, a set of pre-asset networks, and aset of asset-configuration-neural networks. The system further includesat least one server device configured to cause the system to perform theacts of FIG. 8 .

The series of acts 800 includes an act 802 of determining a digitalasset class associated with a digital image. For instance, in one ormore embodiments, the act 802 involves determining, utilizing anasset-recommendation-machine-learning model, a digital asset classassociated with a digital image from among a set of different digitalasset classes. In some embodiments, determining the digital asset classfrom among the set of different digital asset classes comprisesdetermining one of a shape asset class, a color asset class, a patternasset class, or a font asset class.

The series of acts 800 also includes an act 804 of generating a digitalasset corresponding to the digital asset class. To illustrate, in one ormore embodiments, the act 804 involves generating, from the digitalimage and utilizing the asset-recommendation-machine-learning model, adigital asset corresponding to the digital asset class. In someembodiments, generating the digital asset corresponding to the digitalasset class comprises generating a shape asset corresponding to theshape asset class, a color palette asset corresponding to the colorasset class, a color gradient asset corresponding to the color assetclass, a pattern asset corresponding to the pattern asset class, a fontasset corresponding to the font asset class, or a font theme assetcorresponding to the font asset class.

In one or more embodiments, the digital asset recommendation system 106further generates, from the digital image and utilizing theasset-recommendation-machine-learning model, a preprocessed shape assetcorresponding to a shape asset class or a pattern asset class by:detecting a digital object portrayed in the digital image utilizing anobject-detection-neural network; and extracting the digital object fromthe digital image utilizing an object-extraction-neural network.Accordingly, in some embodiments, generating the digital assetcorresponding to the digital asset class comprises generating a shapeasset corresponding to a shape asset class from the preprocessed shapeasset utilizing a black-and-white-pixel-classification-neural network.Further, in some embodiments, generating the digital asset correspondingto the digital asset class comprises generating a pattern assetcorresponding to a pattern asset class from the preprocessed shape assetutilizing a tile-classification-neural network.

In some implementations, the digital asset recommendation system 106further generates, from the digital image and utilizing theasset-recommendation-machine-learning model, a preprocessed color assetcorresponding to a color asset class by extracting a foreground imagelayer from the digital image utilizing aforeground-background-segmentation model. Accordingly, in someembodiments, generating the digital asset corresponding to the digitalasset class comprises generating, utilizing acolor-mood-classification-neural network, a color palette assetcorresponding to the color asset class based on the preprocessed colorasset.

Similarly, in some cases, the digital asset recommendation system 106further generates, from the digital image and utilizing theasset-recommendation-machine-learning model, a preprocessed color assetcorresponding to a color asset class by extracting a background imagelayer from the digital image utilizing aforeground-background-segmentation model. Accordingly, in someinstances, generating the digital asset corresponding to the digitalasset class comprises generating a color gradient asset corresponding tothe color asset class based on the preprocessed color asset.

Further, the series of acts 800 includes an act 806 of generating arecommended digital asset from the digital asset. For example, in someembodiments, the act 806 involves generating, from the digital asset, arecommended digital asset associated with the digital asset class. Inone or more embodiments, generating, from the digital asset, therecommended digital asset associated with the digital asset classcomprises: generating an asset score for the digital asset; andgenerating the recommended digital asset from the digital asset based oncomparing the asset score for the digital asset with one or moreadditional asset scores for one or more additional digital assets.

In one or more embodiments, the series of acts 800 further includes actsfor generating multiple digital assets from a digital image. Forexample, in some cases, the acts include determining, utilizing theasset-recommendation-machine-learning model, an additional digital assetclass associated with the digital image from among the set of differentdigital asset classes; generating, from the digital image and utilizingthe asset-recommendation-machine-learning model, an additional digitalasset corresponding to the digital asset class; and generating, from thedigital asset and for display with the recommended digital asset withina graphical user interface, an additional recommended digital assetassociated with the additional digital asset class.

To provide an illustration, in one or more embodiments, the digitalasset recommendation system 106 determines, utilizing anasset-classification-neural network of anasset-recommendation-machine-learning model, a set of digital assetclasses associated with a digital image; generates, from the digitalimage and utilizing one or more pre-asset networks of theasset-recommendation-machine-learning model, a set of preprocesseddigital assets corresponding to the set of digital asset classes;generates, utilizing an asset-configuration-neural network of theasset-recommendation-machine-learning model, a set of digital assetsfrom the set of preprocessed digital assets; and determines, from theset of digital assets, a set of recommended digital assets associatedwith different digital asset classes.

In one or more embodiments, the digital asset recommendation system 106determines, utilizing the asset-classification-neural network of theasset-recommendation-machine-learning model, the set of digital assetclasses associated with the digital image by generating a firstclassification metric for a shape asset class, a second classificationmetric for a color asset class, and a third classification metric for apattern asset class. In some cases, the digital asset recommendationsystem 106 further determines the set of digital asset classesassociated with the digital image by: determining a text height and textlength of one or more text blocks of the digital image; and generating aclassification metric for a font asset class based on the text heightand text length of the one or more text blocks.

In some embodiments, the digital asset recommendation system 106 furtherdetects one or more user interactions with a graphical user interfacedisplayed on a client device for creating a digital asset from thedigital image; and provides, for display within the graphical userinterface, the set of recommended digital assets with the digital assetin response to the one or more user interactions. In some embodiments,the digital asset recommendation system 106 determines, from the set ofdigital assets, a set of recommended digital assets associated with thedifferent digital asset classes by determining a first set ofrecommended digital assets for the digital image, the first set ofrecommended digital assets associated with a first set of digital assetclasses; and determines, for an additional digital image, additionalrecommended digital assets associated with a second set of digital assetclasses comprising at least one digital asset class not included withinthe first set of digital asset classes. In some cases, the digital assetrecommendation system 106 further determines, utilizing theasset-recommendation-machine-learning model, a digital asset classassociated with an additional digital image; and provides, for displaywithin a graphic user interface of a client device, one or moreinteractive elements for generating a digital asset associated with thedigital asset class from the additional digital image.

To provide another example, in one or more embodiments, the digitalasset recommendation system 106 determines, utilizing anasset-classification-neural network, a set of digital asset classesassociated with a digital image; generates, from the digital image andutilizing at least one pre-asset network from a set of pre-assetnetworks, a set of preprocessed digital assets corresponding to the setof digital asset classes; generates, utilizing at least oneasset-configuration-neural network from a set ofasset-configuration-neural networks, a set of digital assets from theset of preprocessed digital assets; determines an asset score for eachdigital asset from the set of digital assets; and generates, from theset of digital assets, a set of recommended digital assets by selectingdigital assets associated with different digital asset classes based onthe asset score for each digital asset.

In one or more embodiments, the digital asset recommendation system 106determines the asset score for each digital asset from the set ofdigital assets by determining a score value for each digital assetutilizing at least one of the asset-classification-neural network, theat least one pre-asset network, or the at least oneasset-configuration-neural network. In some cases, the digital assetrecommendation system 106 determines a font asset class associated withthe digital image based on text heights and text lengths of text boxesportrayed in the digital image; and generates, from the digital image,at least one font asset based on a text height and text length of a textbox comprising a corresponding font utilizing a text extraction model.

In one or more embodiments, the digital asset recommendation system 106generates, from the digital image and utilizing the at least onepre-asset network, the set of preprocessed digital assets correspondingto the set of digital asset classes by extracting a digital object fromthe digital image utilizing the at least one pre-asset network; andgenerates, utilizing the at least one asset-configuration-neural networkfrom the set of asset-configuration-neural networks, a set of digitalassets from the set of preprocessed digital assets by generating,utilizing the at least one asset-configuration-neural network, one of ashape asset corresponding to a shape asset class or a color paletteasset corresponding to a color asset class based on the digital object.In some cases, the digital asset recommendation system 106 generates,from the digital image and utilizing the at least one pre-asset network,the set of preprocessed digital assets corresponding to the set ofdigital asset classes by extracting a foreground image layer and abackground image layer from the digital image utilizing the at least onepre-asset network; and generates, utilizing the at least oneasset-configuration-neural network from the set ofasset-configuration-neural networks, the set of digital assets from theset of preprocessed digital assets by: generating a color palette assetcorresponding to a color asset class using the foreground image layer;and generating a color gradient asset corresponding to the color assetclass using the background image layer.

Embodiments of the present disclosure may comprise or utilize a specialpurpose or general-purpose computer including computer hardware, suchas, for example, one or more processors and system memory, as discussedin greater detail below. Embodiments within the scope of the presentdisclosure also include physical and other computer-readable media forcarrying or storing computer-executable instructions and/or datastructures. In particular, one or more of the processes described hereinmay be implemented at least in part as instructions embodied in anon-transitory computer-readable medium and executable by one or morecomputing devices (e.g., any of the media content access devicesdescribed herein). In general, a processor (e.g., a microprocessor)receives instructions, from a non-transitory computer-readable medium,(e.g., a memory), and executes those instructions, thereby performingone or more processes, including one or more of the processes describedherein.

Computer-readable media can be any available media that can be accessedby a general purpose or special purpose computer system.Computer-readable media that store computer-executable instructions arenon-transitory computer-readable storage media (devices).Computer-readable media that carry computer-executable instructions aretransmission media. Thus, by way of example, and not limitation,embodiments of the disclosure can comprise at least two distinctlydifferent kinds of computer-readable media: non-transitorycomputer-readable storage media (devices) and transmission media.

Non-transitory computer-readable storage media (devices) includes RAM,ROM, EEPROM, CD-ROM, solid state drives (“SSDs”) (e.g., based on RAM),Flash memory, phase-change memory (“PCM”), other types of memory, otheroptical disk storage, magnetic disk storage or other magnetic storagedevices, or any other medium which can be used to store desired programcode means in the form of computer-executable instructions or datastructures and which can be accessed by a general purpose or specialpurpose computer.

A “network” is defined as one or more data links that enable thetransport of electronic data between computer systems and/or modulesand/or other electronic devices. When information is transferred orprovided over a network or another communications connection (eitherhardwired, wireless, or a combination of hardwired or wireless) to acomputer, the computer properly views the connection as a transmissionmedium. Transmissions media can include a network and/or data linkswhich can be used to carry desired program code means in the form ofcomputer-executable instructions or data structures and which can beaccessed by a general purpose or special purpose computer. Combinationsof the above should also be included within the scope ofcomputer-readable media.

Further, upon reaching various computer system components, program codemeans in the form of computer-executable instructions or data structurescan be transferred automatically from transmission media tonon-transitory computer-readable storage media (devices) (or viceversa). For example, computer-executable instructions or data structuresreceived over a network or data link can be buffered in RAM within anetwork interface module (e.g., a “NIC”), and then eventuallytransferred to computer system RAM and/or to less volatile computerstorage media (devices) at a computer system. Thus, it should beunderstood that non-transitory computer-readable storage media (devices)can be included in computer system components that also (or evenprimarily) utilize transmission media.

Computer-executable instructions comprise, for example, instructions anddata which, when executed by a processor, cause a general-purposecomputer, special purpose computer, or special purpose processing deviceto perform a certain function or group of functions. In someembodiments, computer-executable instructions are executed on ageneral-purpose computer to turn the general-purpose computer into aspecial purpose computer implementing elements of the disclosure. Thecomputer executable instructions may be, for example, binaries,intermediate format instructions such as assembly language, or evensource code. Although the subject matter has been described in languagespecific to structural features and/or methodological acts, it is to beunderstood that the subject matter defined in the appended claims is notnecessarily limited to the described features or acts described above.Rather, the described features and acts are disclosed as example formsof implementing the claims.

Those skilled in the art will appreciate that the disclosure may bepracticed in network computing environments with many types of computersystem configurations, including, personal computers, desktop computers,laptop computers, message processors, hand-held devices, multiprocessorsystems, microprocessor-based or programmable consumer electronics,network PCs, minicomputers, mainframe computers, mobile telephones,PDAs, tablets, pagers, routers, switches, and the like. The disclosuremay also be practiced in distributed system environments where local andremote computer systems, which are linked (either by hardwired datalinks, wireless data links, or by a combination of hardwired andwireless data links) through a network, both perform tasks. In adistributed system environment, program modules may be located in bothlocal and remote memory storage devices.

Embodiments of the present disclosure can also be implemented in cloudcomputing environments. In this description, “cloud computing” isdefined as a model for enabling on-demand network access to a sharedpool of configurable computing resources. For example, cloud computingcan be employed in the marketplace to offer ubiquitous and convenienton-demand access to the shared pool of configurable computing resources.The shared pool of configurable computing resources can be rapidlyprovisioned via virtualization and released with low management effortor service provider interaction, and then scaled accordingly.

A cloud-computing model can be composed of various characteristics suchas, for example, on-demand self-service, broad network access, resourcepooling, rapid elasticity, measured service, and so forth. Acloud-computing model can also expose various service models, such as,for example, Software as a Service (“SaaS”), Platform as a Service(“PaaS”), and Infrastructure as a Service (“IaaS”). A cloud-computingmodel can also be deployed using different deployment models such asprivate cloud, community cloud, public cloud, hybrid cloud, and soforth. In this description and in the claims, a “cloud-computingenvironment” is an environment in which cloud computing is employed.

FIG. 9 illustrates a block diagram of an example computing device 900that may be configured to perform one or more of the processes describedabove. One will appreciate that one or more computing devices, such asthe computing device 900 may represent the computing devices describedabove (e.g., the server(s) 102 and/or the client devices 110 a-110 n).In one or more embodiments, the computing device 900 may be a mobiledevice (e.g., a mobile telephone, a smartphone, a PDA, a tablet, alaptop, a camera, a tracker, a watch, a wearable device). In someembodiments, the computing device 900 may be a non-mobile device (e.g.,a desktop computer or another type of client device). Further, thecomputing device 900 may be a server device that includes cloud-basedprocessing and storage capabilities.

As shown in FIG. 9 , the computing device 900 can include one or moreprocessor(s) 902, memory 904, a storage device 906, input/outputinterfaces 908 (or “I/O interfaces 908”), and a communication interface910, which may be communicatively coupled by way of a communicationinfrastructure (e.g., bus 912). While the computing device 900 is shownin FIG. 9 , the components illustrated in FIG. 9 are not intended to belimiting. Additional or alternative components may be used in otherembodiments. Furthermore, in certain embodiments, the computing device900 includes fewer components than those shown in FIG. 9 . Components ofthe computing device 900 shown in FIG. 9 will now be described inadditional detail.

In particular embodiments, the processor(s) 902 includes hardware forexecuting instructions, such as those making up a computer program. Asan example, and not by way of limitation, to execute instructions, theprocessor(s) 902 may retrieve (or fetch) the instructions from aninternal register, an internal cache, memory 904, or a storage device906 and decode and execute them.

The computing device 900 includes memory 904, which is coupled to theprocessor(s) 902. The memory 904 may be used for storing data, metadata,and programs for execution by the processor(s). The memory 904 mayinclude one or more of volatile and non-volatile memories, such asRandom-Access Memory (“RAM”), Read-Only Memory (“ROM”), a solid-statedisk (“SSD”), Flash, Phase Change Memory (“PCM”), or other types of datastorage. The memory 904 may be internal or distributed memory.

The computing device 900 includes a storage device 906 including storagefor storing data or instructions. As an example, and not by way oflimitation, the storage device 906 can include a non-transitory storagemedium described above. The storage device 906 may include a hard diskdrive (HDD), flash memory, a Universal Serial Bus (USB) drive or acombination these or other storage devices.

As shown, the computing device 900 includes one or more I/O interfaces908, which are provided to allow a user to provide input to (such asuser strokes), receive output from, and otherwise transfer data to andfrom the computing device 900. These I/O interfaces 908 may include amouse, keypad or a keyboard, a touch screen, camera, optical scanner,network interface, modem, other known I/O devices or a combination ofsuch I/O interfaces 908. The touch screen may be activated with a stylusor a finger.

The I/O interfaces 908 may include one or more devices for presentingoutput to a user, including, but not limited to, a graphics engine, adisplay (e.g., a display screen), one or more output drivers (e.g.,display drivers), one or more audio speakers, and one or more audiodrivers. In certain embodiments, I/O interfaces 908 are configured toprovide graphical data to a display for presentation to a user. Thegraphical data may be representative of one or more graphical userinterfaces and/or any other graphical content as may serve a particularimplementation.

The computing device 900 can further include a communication interface910. The communication interface 910 can include hardware, software, orboth. The communication interface 910 provides one or more interfacesfor communication (such as, for example, packet-based communication)between the computing device and one or more other computing devices orone or more networks. As an example, and not by way of limitation,communication interface 910 may include a network interface controller(NIC) or network adapter for communicating with an Ethernet or otherwire-based network or a wireless NIC (WNIC) or wireless adapter forcommunicating with a wireless network, such as a WI-FI. The computingdevice 900 can further include a bus 912. The bus 912 can includehardware, software, or both that connects components of computing device900 to each other.

In the foregoing specification, the invention has been described withreference to specific example embodiments thereof. Various embodimentsand aspects of the invention(s) are described with reference to detailsdiscussed herein, and the accompanying drawings illustrate the variousembodiments. The description above and drawings are illustrative of theinvention and are not to be construed as limiting the invention.Numerous specific details are described to provide a thoroughunderstanding of various embodiments of the present invention.

The present invention may be embodied in other specific forms withoutdeparting from its spirit or essential characteristics. The describedembodiments are to be considered in all respects only as illustrativeand not restrictive. For example, the methods described herein may beperformed with less or more steps/acts or the steps/acts may beperformed in differing orders. Additionally, the steps/acts describedherein may be repeated or performed in parallel to one another or inparallel to different instances of the same or similar steps/acts. Thescope of the invention is, therefore, indicated by the appended claimsrather than by the foregoing description. All changes that come withinthe meaning and range of equivalency of the claims are to be embracedwithin their scope.

What is claimed is:
 1. In a digital medium environment for digitaldesign, a computer-implemented method for generating recommended digitalassets comprising: determining, utilizing anasset-recommendation-machine-learning model, a digital asset classassociated with a digital image from among a set of different digitalasset classes; generating, from the digital image and utilizing theasset-recommendation-machine-learning model, a digital assetcorresponding to the digital asset class; and generating, from thedigital asset, a recommended digital asset associated with the digitalasset class.
 2. The computer-implemented method of claim 1, wherein:determining the digital asset class from among the set of differentdigital asset classes comprises determining one of a shape asset class,a color asset class, a pattern asset class, or a font asset class; andgenerating the digital asset corresponding to the digital asset classcomprises generating a shape asset corresponding to the shape assetclass, a color palette asset corresponding to the color asset class, acolor gradient asset corresponding to the color asset class, a patternasset corresponding to the pattern asset class, a font assetcorresponding to the font asset class, or a font theme assetcorresponding to the font asset class.
 3. The computer-implementedmethod of claim 1, further comprising generating, from the digital imageand utilizing the asset-recommendation-machine-learning model, apreprocessed shape asset corresponding to a shape asset class or apattern asset class by: detecting a digital object portrayed in thedigital image utilizing an object-detection-neural network; andextracting the digital object from the digital image utilizing anobject-extraction-neural network.
 4. The computer-implemented method ofclaim 3, wherein generating the digital asset corresponding to thedigital asset class comprises generating a shape asset corresponding toa shape asset class from the preprocessed shape asset utilizing ablack-and-white-pixel-classification-neural network.
 5. Thecomputer-implemented method of claim 3, wherein generating the digitalasset corresponding to the digital asset class comprises generating apattern asset corresponding to a pattern asset class from thepreprocessed shape asset utilizing a tile-classification-neural network.6. The computer-implemented method of claim 1, further comprisinggenerating, from the digital image and utilizing theasset-recommendation-machine-learning model, a preprocessed color assetcorresponding to a color asset class by extracting a foreground imagelayer from the digital image utilizing aforeground-background-segmentation model, wherein generating the digitalasset corresponding to the digital asset class comprises generating,utilizing a color-mood-classification-neural network, a color paletteasset corresponding to the color asset class based on the preprocessedcolor asset.
 7. The computer-implemented method of claim 1, furthercomprising generating, from the digital image and utilizing theasset-recommendation-machine-learning model, a preprocessed color assetcorresponding to a color asset class by extracting a background imagelayer from the digital image utilizing aforeground-background-segmentation model, wherein generating the digitalasset corresponding to the digital asset class comprises generating acolor gradient asset corresponding to the color asset class based on thepreprocessed color asset.
 8. The computer-implemented method of claim 1,wherein generating, from the digital asset, the recommended digitalasset associated with the digital asset class comprises: generating anasset score for the digital asset; and generating the recommendeddigital asset from the digital asset based on comparing the asset scorefor the digital asset with one or more additional asset scores for oneor more additional digital assets.
 9. The computer-implemented method ofclaim 1, further comprising: determining, utilizing theasset-recommendation-machine-learning model, an additional digital assetclass associated with the digital image from among the set of differentdigital asset classes; generating, from the digital image and utilizingthe asset-recommendation-machine-learning model, an additional digitalasset corresponding to the digital asset class; and generating, from thedigital asset and for display with the recommended digital asset withina graphical user interface, an additional recommended digital assetassociated with the additional digital asset class.
 10. A non-transitorycomputer-readable medium storing instructions thereon that, whenexecuted by at least one processor, cause a computing device to:determine, utilizing an asset-classification-neural network of anasset-recommendation-machine-learning model, a set of digital assetclasses associated with a digital image; generate, from the digitalimage and utilizing one or more pre-asset networks of theasset-recommendation-machine-learning model, a set of preprocesseddigital assets corresponding to the set of digital asset classes;generate, utilizing an asset-configuration-neural network of theasset-recommendation-machine-learning model, a set of digital assetsfrom the set of preprocessed digital assets; and determine, from the setof digital assets, a set of recommended digital assets associated withdifferent digital asset classes.
 11. The non-transitorycomputer-readable medium of claim 10, further comprising instructionsthat, when executed by the at least one processor, cause the computingdevice to determine, utilizing the asset-classification-neural networkof the asset-recommendation-machine-learning model, the set of digitalasset classes associated with the digital image by generating a firstclassification metric for a shape asset class, a second classificationmetric for a color asset class, and a third classification metric for apattern asset class.
 12. The non-transitory computer-readable medium ofclaim 10, further comprising instructions that, when executed by the atleast one processor, cause the computing device to determine the set ofdigital asset classes associated with the digital image by: determininga text height and text length of one or more text blocks of the digitalimage; and generating a classification metric for a font asset classbased on the text height and text length of the one or more text blocks.13. The non-transitory computer-readable medium of claim 10 furthercomprising instructions that, when executed by the at least oneprocessor, cause the computing device to: detect one or more userinteractions with a graphical user interface displayed on a clientdevice for creating a digital asset from the digital image; and provide,for display within the graphical user interface, the set of recommendeddigital assets with the digital asset in response to the one or moreuser interactions.
 14. The non-transitory computer-readable medium ofclaim 10 further comprising instructions that, when executed by the atleast one processor, cause the computing device to: determine, from theset of digital assets, a set of recommended digital assets associatedwith the different digital asset classes by determining a first set ofrecommended digital assets for the digital image, the first set ofrecommended digital assets associated with a first set of digital assetclasses; and determine, for an additional digital image, additionalrecommended digital assets associated with a second set of digital assetclasses comprising at least one digital asset class not included withinthe first set of digital asset classes.
 15. The non-transitorycomputer-readable medium of claim 10, further comprising instructionsthat, when executed by the at least one processor, cause the computingdevice to: determine, utilizing theasset-recommendation-machine-learning model, a digital asset classassociated with an additional digital image; and provide, for displaywithin a graphic user interface of a client device, one or moreinteractive elements for generating a digital asset associated with thedigital asset class from the additional digital image.
 16. A systemcomprising: at least one memory device comprising anasset-recommendation-machine-learning model comprising anasset-classification-neural network, a set of pre-asset networks, and aset of asset-configuration-neural networks; and at least one serverdevice configured to cause the system to: determine, utilizing theasset-classification-neural network, a set of digital asset classesassociated with a digital image; generate, from the digital image andutilizing at least one pre-asset network from the set of pre-assetnetworks, a set of preprocessed digital assets corresponding to the setof digital asset classes; generate, utilizing at least oneasset-configuration-neural network from the set ofasset-configuration-neural networks, a set of digital assets from theset of preprocessed digital assets; determine an asset score for eachdigital asset from the set of digital assets; and generate, from the setof digital assets, a set of recommended digital assets by selectingdigital assets associated with different digital asset classes based onthe asset score for each digital asset.
 17. The system of claim 16,wherein the at least one server device is configured to cause the systemto determine the asset score for each digital asset from the set ofdigital assets by determining a score value for each digital assetutilizing at least one of the asset-classification-neural network, theat least one pre-asset network, or the at least oneasset-configuration-neural network.
 18. The system of claim 16, whereinthe at least one server device is further configured to cause the systemto: determine a font asset class associated with the digital image basedon text heights and text lengths of text boxes portrayed in the digitalimage; and generate, from the digital image, at least one font assetbased on a text height and text length of a text box comprising acorresponding font utilizing a text extraction model.
 19. The system ofclaim 16, wherein the at least one server device is configured to causethe system to: generate, from the digital image and utilizing the atleast one pre-asset network, the set of preprocessed digital assetscorresponding to the set of digital asset classes by extracting adigital object from the digital image utilizing the at least onepre-asset network; and generate, utilizing the at least oneasset-configuration-neural network from the set ofasset-configuration-neural networks, a set of digital assets from theset of preprocessed digital assets by generating, utilizing the at leastone asset-configuration-neural network, one of a shape assetcorresponding to a shape asset class or a color palette assetcorresponding to a color asset class based on the digital object. 20.The system of claim 16, wherein the at least one server device isconfigured to cause the system to: generate, from the digital image andutilizing the at least one pre-asset network, the set of preprocesseddigital assets corresponding to the set of digital asset classes byextracting a foreground image layer and a background image layer fromthe digital image utilizing the at least one pre-asset network; andgenerate, utilizing the at least one asset-configuration-neural networkfrom the set of asset-configuration-neural networks, the set of digitalassets from the set of preprocessed digital assets by: generating acolor palette asset corresponding to a color asset class using theforeground image layer; and generating a color gradient assetcorresponding to the color asset class using the background image layer.